DEPARTMENT OF COASTAL & OFFSHORE ENGINEERING. School of ... This is to certify that this dissertation entitled âApplication of Fuzzy Logic. Model for .... policies for sustainable development and as a guideline for resource allocation for.
APPLICATION OF FUZZY LOGIC MODEL FOR THE ASSESSMENT OF COASTAL VULNERABILITY ALONG THE KERALA COAST DISSERTATION SUBMITTED TO
KERALA UNIVERSITY OF FISHERES AND OCEAN STUDIES In partial fulfillment of the requirements for the award of MASTER OF TECHNOLOGY (M.Tech) IN
Integrated Coastal Zone Management Submitted by
ANJU K Register No: OST-2014-25-02
Department of Coastal & Offshore Engineering School of Ocean Engineering and Underwater Technology KERALA UNIVERSITY OF FISHERES AND OCEAN STUDIES PANANGAD, KOCHI-682 506
NANSEN ENVIRONMENTAL RESEARCH CENTRE (NERCI), INDIA AUGUST 2016
CERTIFICATE
This is to certify that this dissertation entitled “Application of Fuzzy Logic Model for the Assessment of Coastal Vulnerability along the Kerala Coast” is a bonafide record done by ANJU K. (Register No: OST-2014-25-02) under my supervision and guidance from 8th August 2015 to 1st August 2016, In partial fulfillment of the requirements for the award of Master of Technology (M.Tech) and that no part thereof has been presented before any degree, diploma or similar titles.
Dr. K. Ajith Joseph Principal Scientist and Executive Director NERCI (Supervisor in charge)
Dr. M.K. Sajeevan Director School of Ocean Engineering and Underwater Technology
Nansen Environmental Research Centre (NERCI) India th
st
Duration –8 August 2015 to 1 August 2016
DECLARATION
I hereby declare that the project entitled “APPLICATION OF FUZZY LOGIC MODEL FOR THE ASSESSMENT OF COASTAL VULNERABILITY ALONG THE KERALA COAST “ submitted for the M.Tech (ICZM) degree is my original work and the project has not formed the basis for the award of any other degree, diploma, fellowship or any other similarities .
Place: Panangad Date: 16-08-2016
Signature of the student
DEPARTMENT OF COASTAL AND OFFSHORE ENGINEERING KERALA UNIVERSITY OF FISHERIES AND OCEAN STUDIES
Bona fide Certificate This is to certify that this report titled “Application of Fuzzy Logic Model for the Assessment of Coastal Vulnerability Along the Kerala Coast “is a bona fide record of the project presented by ANJU K (OST-2014-25-02) towards the partial fulfillment of the requirement for the award of M.Tech Degree in Integrated Coastal Zone Management of the Kerala university of Fisheries and Ocean Studies during the year 2016. Certified further that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate.
External examiner 1
External examiner 2
External examiner 3
ACKNOWLEDGMENT
While bringing out this report to its final from, I came across a number of people whose contributions in various ways helped to complete the work and they deserve special thanks. It is a pleasure to convey my gratitude to all of them. First and foremost, I would like to express my deep sense of gratitude and indebtedness to my guide Dr. K. Ajith Joseph for his invaluable encouragement, suggestions and support from the early stage of the project and providing me extraordinary experiences throughout the work. Above all, his priceless and meticulous supervision at each and every phase of work inspired me in innumerable ways. I am extremely grateful to Dr. Satheesh Sadasivam (Post-Doctoral fellow, Department of Civil and Environmental Engineering, UAEU University, UAE), Dr. Sunny Josph Kalayathankal (Associate Professor, K E College Mannanam), Ms. Maya (AE Harbor Engineering Department Tiruvananthpuram), and Mr. Jayadeep (AE Harbor Engineering Department Kozikode). I am obliged to all the project assistants for their support and cooperation that is difficult to express in words. I thank my friends especially Reba Mary Raju, Rakhi, Muhamad, Aby and Nowshad for their moral support. Finally, I am deeply indebted to my parents for their moral support and continues encouragement carrying out this study.
Anju K
ABSTRACT Natural and human systems of the coastal zone are vulnerable to climate change and its various consequences. Decisions about how to address climate change can be complex, and responses will require a combination of adaptation and mitigation actions. Decision makers whether individuals, public officials, or others may need to take much effort to integrating scientific information into adaptation and mitigation decisions. Today many decision support technologies, processes and tools are available, which enable decision-makers to identify and assess response options, apply complex and uncertain information, clarify tradeoffs, strengthen transparency, and generate information on the costs and benefits of different choices.
Decision support systems can be developed to improve the understanding of the interrelationships between the natural and socio-economic variables. This study introduces Fuzzy logic (FL) decision-making framework that are useful for considering choices about climate change responses through the complementary strategies of adaptation and mitigation. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO. Fuzzy Logic Systems produce acceptable but definite output in response to incomplete, ambiguous, distorted, or inaccurate (fuzzy) input. In this study, outcome-driven fuzzy logic approach has been attempted to develop a coastal vulnerability assessment model for Kerala region to sea level rise. Based on this model and the region selected for vulnerable assessment study, it is identified that the coasts along Azheekal, Arattupuzha, Fort Kochi and Cherayi are highly vulnerable to the impacts of Sealevel rise whereas the medium vulnerable areas are the coasts along Kollam, Kozhikodu, Poovar and Cherthala. The low vulnerable coasts along the Kerala coast are Thrissur, Ponnanni, Bekal and Payyambalam.
CONTENTS Chapter I 1.1 1.2 1.3 1.4 1.5 1.5.1 1.5.2 1.6 1.7 1.8 Chapter II 2.1 2.2 2.3 2.4 2.5
Introduction Introduction The climate system and climate change Problem statement Aim and Objective of the project Fuzzy logic Fuzzy Vulnerability Assessment Model Five basic steps for building and simulate fuzzy logic system Scope of the study Significance of the study Study Area
Page No. 1 2 5 7 7 7 8 8 9 10
Literature Review Introduction Assessment types Coastal vulnerability index (CVI) Indicator-based approach GIS-based decision support systems (DSS) DEYSCO and DITTY
17 20 20 24 27
2.6
Methods based on dynamic computer models- DIVA, SimCLIM, RegIS and Delft3D
28
2.7
Coastal Vulnerability Assessment and Mapping- A review of real life application
30
2.8 2.9 2.1
Analytical hierarchy process (AHP) for the fuzzy vulnerability assessment model Making Fuzzy Decisions, problem statement Existing fuzzy logic decision making system
33 34 36
Chapter III
Data and Methodology
3.1
Data Collection
42
3.2
Methodology using Fuzzy logic toolbox in mat lab
44
3.3 3.4 3.4 3.5
Fuzzy logic model Kerala Coast Coastal Vulnerability Index Data Ranking
44 45 46 46
Chapter IV 4.1 4.2
Result Fuzzy vulnerability assessment model result Methodology
48 49
Chapter V
Discussion
62
Chapter VI
Summary
67
6.1 Chapter VII
Future Outlook and Recommendations Reference
69 71
LIST OF FIGURES Page No. Figure 1.2.1
The climate system.
3
Figure 1.2.2
The climate subsystems and relevant interactions.
3
Figure 1.2.3
Global Annual Mean Surface Air Temperature Change.
4
Figure 1.2.4
Yearly temperature anomalies from 1880 to 2014.
4
Figure 1.2.5
Global Mean Sea Level
5
Figure 1.5.1
Fuzzy vulnerability assessment model structure
8
Figure 2.1.0
Block scheme of the DITTY-DSS
28
Figure 3.1.0
Block diagram: Flow Chart of Project Activities
45
Figure 4.1.0
Block diagram: flowchart of CVI calculation
48
Figure 4.2.0
Define input and output
52
Figure 4.3.0 Figure 4.4.0 Figure 4.5.0
Figure 4.6.0
Figure 4.7.0
Figure 4.8.0
Figure 4.9.0 Figure 4.10.0
Go to edit-Add variable input, here set as 3 input variable Name each input variable Go to edit and delete existing membership function and add new membership function Define membership function for input-1, add values& ranges Define membership function for input-2, add values& ranges Define membership function for input-3, add values& ranges Define membership function for output, add values& ranges Define rule- close membership function and go to edit
52 53 53
54
54
55
55 56
the rule Figure 4.11.0
Go to view menu and select rules will get rule viewer interface
56
Figure 4.12.0
Rule viewer interface
57
Figure 4.13.0
Simulation in surface viewer 1
57
Figure 4.14.0
Simulation in surface viewer 2
58
Figure 4.15.0
Simulation in surface viewer 3
58
Figure 4.16.0
Simulation in surface viewer 4
59
Figure 4.17.0
Simulation in surface viewer 5
59
Figure 4.18.0
Simulation in surface viewer 6
60
Figure 4.19.0
Simulation in surface viewer 7
60
Figure 5.01.0
Over all procedure for fuzzy logic system
63
LIST OF TABLE
Table Table
1.1 2.1
Table
2.2
Table Table
3.1 4.1
Table
4.2
Table
4.3
Table Table
5.1 5.2
Kerala coastal areas Coastal vulnerability index (CVI) formula Indicator based approach adopted by the European Environment Agency Data Parameters RI values for different order of the matrix Coastal vulnerability ranking based on the fuzzy model output Vulnerability assessment of selected beaches along the Kerala coast using fuzzy model Vulnerability ranking criteria for physical variables Weights obtained from AHP process
Page No. 16 22 26 43 49 61 61 65 65
CHAPTER-I INTRODUCTION
1.1. Introduction Global warming and climate change has become one of the major problems of this century that would continue to pose a major threat for all major systems of the earth. The vulnerability is strongly increased and hence mitigation and adaptation measures have to be taken at the earliest. Coastal zones are especially under threat due to the impact of global sea level rise regulating from climate change. The need for management of the risks associated with global sea level rise has initiated many studies around the world for a variety of subsystems in the coastal areas like ecological assessments, coastal vulnerability assessments by using physical parameters such as coastal erosion, Slope, Elevation, Sea Level Rise, Shoreline Change Rate, high Tidal Range, engineering solutions and adaptation of present coastal protection measures, and socio-economic systems. Although research on different sub-systems are ongoing to face the coastal vulnerability, Coastal areas are under threat due to many driving factors such as high urbanization, climate change and unsustainable use of available coastal resources. The impacts of climate change are sea level rise, Increase in water temperature and acidification, inundation, flooding leads to increase in coastal erosion rates added stresses for eco-systems and ultimately threats to properties, infrastructures and the livelihood of communities that depends on coasts worldwide. Coastal vulnerability assessment would provide a detailed quantitative estimates for each type of impacts that help to identifying multiple and growing problems in the coastal zone.
The concept behind the idea of integrated coastal zone management (ICZM) is sustainability. The process for the management of the coast covers the full cycle of information collection, planning (in its broad sense), decision making, management and monitoring of implementation. 1
Kerala is situated along the southwest coast of India and has long sea shore of 590 km which has sandy beaches, mud banks, rocky cliffs, lagoons, estuaries and barrier islands, also having highest density of population in the coastal belt. Thus, a coastal vulnerability assessment of Kerala to sea level rise is needed both as a part of coastal zone management policies for sustainable development and as a guideline for resource allocation for preparation of adaptation options for upcoming problems due to sea level rise. Prediction and decision making has vital role to maintain the sustainability. Decision-making for integrated coastal management involves multiple decision-makers and multiple stakeholders often with conflicting needs and interests.
The fuzzy coastal vulnerability index (FCVI) assessment model enables decision makers to compare and rank different regions according to its vulnerabilities to sea level rise, to prioritize the impacts of sea level rise on the region with respect to the vulnerability of the region to each impact and also to determine the most vulnerable parameters for planning of adaptation measures to sea level rise. This in turn, would increase the confidence level of adaptation measures and as well as accelerate the implementation of these measures to coastal areas against climate change. The present study is in accordance with the general approach in studying the impacts of sea level rise at different geomorphological coastal regions and hence the applicability of this proposed model (FCVI) for the Kerala coast is attempted.
1.2. The climate system and climate change The elements of the climate system include the atmosphere, the hydrosphere, the cryosphere (ice and snow), the pedosphere (soil), the lithosphere (rocks), and the biosphere (animals and plants). The climate system components interact at many spatial and temporal scales. All of these elements together compose the Earth’s climate system. Influenced by many factors, including solar radiation, wind, and ocean currents, Earth’s climate system is a very complex framework The climate system, figure (1.2.1) adopted from (Ayman, 2015) which is decorated with various sub systems. 2
Figure 1.2.1. The climate system (Source: CSIRO Division of Atmospheric Research)
Figure 1.2.2 The climate subsystems and relevant interactions (Reference http://worldoceanreview.com/en/wor-1/climate-system/earth-climate-system/) 3
Figure 1.2.3 Global Annual Mean Surface Air Temperature Change (Reference fromhttp://data.giss.nasa.gov/gistemp/graphs_v3/)
From the figure 1.2.3, it is found that the global temperature of surface air is observed to increase during the period 1880 to 2014. Despite different approaches, baseline period and their own methods to estimate global temperatures, NASA, NOAA, Japan Meteorological Agency and the Met Office Hadley Centre in the United Kingdom all observed similar records in the global temperature. Based on NASA Earth Observatory records (Figur.1.2.3) the yearly temperature anomalies from 1880 to 2014 and the figure (1.2.4) as recorded by those four institutions. With minor variation in their secondary observed rapid warming in the past few decades, and defined that the last decade as the warmest as evidenced from figure (1.2.4)
Figure 1.2.4 Yearly temperature anomalies from 1880 to 2014 (Reference fromhttp://earthobservatory.nasa.gov)
4
Figure 1.2.5 Global Mean Sea Level (Reference fromhttp://www.coolaustralia.org/)
Similarly the global mean sea level was also observed to increase from1870 to 2010 as analysed from tide gauge data and satellite altirneter data (figure 1.2.5).
1.3. Problem statement Vulnerability covers exposure to risk, hazards, shocks and stress, difficulty in coping with contingencies and access to assets. It refers to a situation when certain groups in society are more vulnerable than others to shocks that threaten their livelihood and/or survival. Vulnerability is defined as the susceptibility of human and environmental systems that are likely to experience harm or damage from the exposure to stresses and from the absence of capacity to adapt. As a result of population growth the urbanization and the vulnerability of coastal areas has been increased considerably due to agglomeration of people towards coast. The vulnerability of coastal areas are often at very high risk and besides coastal ecosystems, there are now many human and economic resources at risk. Destructive development practices may further increase the vulnerability of an area. Mining of beaches for construction became lowering coastal dunes to enhance the view and destruction of mangroves, vegetation and natural protective barriers render these coastal areas more
5
vulnerable to natural hazards. Some of the major coastal hazards are tropical storms and hurricanes, storm surges, Tsunamis, flooding, landslides, volcanic eruptions and earthquakes.
Predicting the future conditions of the land and sea resources that the coastal areas offer to human population has become one of the main problems that concern both national and local decision makers. The dynamic and complex physical processes such as sediment transport or coastal flooding drive changes in the coastal areas. These processes can only be predicted up to a certain level even if with recent scientific advancement. Within every moment uncertainty increases from the natural sub-system to the human sub-system. The concept of coastal zone management to ensure sustainable development of coastal areas was initiated by the high demand and the uncontrolled use of these resources by various stakeholders. There are many problems that the decision makers have to face while implementing coastal zone management practices as that the outcomes of the decisions regarding the complex physical processes would not have bearded as expected. On top of all the uncertainties and conflicts that are present at coastal areas, impacts of climate change, associated with high uncertainty, turn decision making into a risk management process.
Coastal zone management includes coastal system modeling that developed by using integrated assessment studies. In the context of climate change it has a fairly new application mostly due to many inherent uncertainties related to the interactions between earth systems and human systems and the climate change itself. On the other hand, importance of integration of earth and human systems has been highlighted as more research is undertaken which shows that the main actors of the framework of climate change are parts of forward and backward feeding cycles. Several research initiatives were stated by ‘Intergovernmental Panel on Climate Change,(IPCC 2007a)’ which strongly agrees with the understanding that limitations of available data, limitations of knowledge on especially interaction and integration of human and natural subsystems as well as limitations of integration of uncertainty by decision makers to actual policy making processes (adaptation options) are the key sources of problems that need to be overcome by the scientific approaches when coastal zones are considered. The limitation on available data is especially an important problem for in coastal areas where long term coastal data for most locations, does not exist. The quality of available data is another uncertainty due to many other factors such as the location of 6
meteorological stations, calibration of the measuring devices and the duration of measurements including availability of human and budget capacity.
1.4. Aim and Objective of the study To understand the cause for causality in the coastal region that may arise due to coastal hazards as per the past evidence and present analysis of the coastal region. The proposed study, would assess the susceptibility of coastal community and environmental systems at selected locations of the coast of Kerala that are likely to experience harm or damage from the exposure to sea level rise, flooding and coastal erosion in the context of the absence of capacity to adapt, through application of fuzzy logic system.
1.5. Fuzzy logic Fuzzy logic, presented by Zadeh [1965], is a form of multi-valued logic which relates to fuzzy set theory in contrast with classical set theory, in order to deal with approximate reasoning. Fuzzy logic is particularly adapted for the environmental problems because of the high level of uncertainty and approximation associated with environment. Vulnerability assessment can be attempted based on Multi-criteria decision analysis and fuzzy logic techniques. Prediction modules can be considered by using the information regarding the present situation and the application of fuzzy logic based algorithms for predicting future scenarios.
The approach to effective problem solving system involves the development of Decision Support Systems (DSSs). A DSS is a system for helping to choose among alternative actions in order to support decision-making for a specific problem or type of problems. To solve such multifaceted problems, the use of Artificial Intelligence based models is widely adopted by fuzzy logic based Multi-criteria decision analysis. 1.5.1
FUZZY VULNERABILITY ASSESSMENT MODEL
The fuzzy vulnerability assessment model is applied to serve as a bridge between earth and human systems. The values of important parameters in fuzzy modeling algorithms are selected by using fuzzy decision making. Fuzzy model generate with the help of graphical user interface which is developed on MATLAB platform. 7
1.5.2
Five basic steps for building and simulate fuzzy logic system are follows: 1. Define inputs and outputs. 2. Create membership function. 3. Create fuzzy rules. 4. Simulate the result in fuzzy logic system. 5. Generate Fuzzy Model. Input Values (Site Specific)
Physical Inference System of an Impact
Human Inference System of an Impact
Physical Vulnerability (Defuzzified values)
Human Vulnerability (Defuzzified values)
Impact Inference System Impact Vulnerability Scores (crisp) & Impact Fuzzy Sets (input)
Impact Vulnerability Scores (crisp) & Impact Fuzzy Sets (input) Coastal Vulnerability Inference System
Coastal Vulnerability Index
Figure 1.5.1. Fuzzy vulnerability assessment model structure
1.6. Scope of the study This study analysis present adaptation measures and limitations of vulnerability assessment of Kerala Coast with the help of fuzzy Logic approach.
8
A study area was chosen. The impact of sea and sea level rise was investigated. The vulnerability and adaptation of the study area was examined. The vulnerability indicators of the study area were examined. The available techniques were investigated and the fuzzy decision making technique (FDMT) as chosen to be incorporated to the study area. The FDMT was adopted and applied to the coastal regions. Since this technique is suitable in evaluating situations that deal with ambiguity and vagueness, involve subjectivity, and imprecise information. As the fuzzy decision making technique enables maximum benefit from practical know-how by taking into account several variables and it could perform “weighted merging” to influencing variables. Hence this approach is taken for the present study.
1.7. Significance of the study Significance: One of the coastal problems to be considered in Kerala is mainly coastal erosion. About 80% of the entire coastline of Kerala is affected by long term coastal erosion and part of the coastline of Kerala is affected by erosion and accretion in from monsoon and non-monsoon months. Natural factors that cause erosion in Kerala are heavy rainfall, loose sandy sea shore, destruction of mud deposits in the sea, and heavy discharge of water devoid of alluvium.
Potential benefits: The coastal zone can be subjected to the impact of climate change (i.e. sea level rise). Traditionally, the adaptation measures that were identified to deal with the impact of climate change on coastal zone areas included beach nourishment, construction of groins and breakwaters, tightening legal regulations, integrated coastal zone management and introducing changes in land use. These traditional measures are considered to be obsolete. For that reason, a multi-criterion analysis, in ranking the effectiveness of the adaptation strategy, was proposed through this fuzzy decision making.
Overall impact of the study: Here Fuzzy logic is applied to take decision towards the assessment of coastal vulnerability into its likely on in impacts in Kerala coast by choosing parameters that cause coastal vulnerability direct to find out influence of indirect coastal
9
parameters. Priorities coastal areas according to their vulnerability to sea level rise extended would help authorities to make decision properly. Coastal Vulnerability Index The computation of coastal vulnerability index involves the estimation of physical vulnerability index (PVI) and social vulnerability index (SVI). The weights for PVI and SVI were then calculated using the analytic hierarchy process. CVI can be further computed using the calculated indices to understand the relative vulnerability of the study area. A detailed study can consider nine variables that can be classified into two groups: 1) physical variables and 2) social variables. The physical variables include tidal range, significant wave height, sea level rise, shoreline change rate, slope and elevation. The social variables include population density, land use and road network. These parameters can be derived from GIS analysis, remote sensing data and field data pertaining to the study area under consideration. The schematic diagram showing the methodology used in the study is shown in Fig.1.7.1 with the coastal vulnerability subdivided into two components namely physical vulnerability and social vulnerability along with the associated variables used for its computation..
Data Ranking The values of variables were assigned a vulnerability ranking based on value ranges from vulnerability point of view. Each variable was ranked from 0 to 1.0 representing very low, low, high and very high respectively. In other words, a value of 0 represents the lowest risk and 1 represents the highest risk. The database includes both quantitative and qualitative information. Thus, numerical variables are assigned a risk ranking based on data value ranges, while the non-numerical land use variable is ranked according to their relative resistance of a given landform to vulnerability.
1.8 . STUDY AREA Kerala Coast Kerala is situated along the southwest coast of India and has long coast line of 580 km that includes sandy beaches, mud banks, rocky cliffs, lagoons, estuaries and barrier islands. Kerala has the highest concentration of people living in the coastal belt. Thus the major 10
issues related to the coastal erosion, degradation of coastal vegetation, coastal resource management and intense economic activities in Kerala warrants special attention.
Kerala (38,863 km²; 1.18% of India’s landmass) is situated between the Arabian Sea to the west and the Western Ghats to the east. Kerala’s coast runs some 580 km in length, while the state itself varies between 35 KM to 120 KM in width. Geographically, Kerala roughly divides into three climatically distinct regions. These include the eastern highlands (rugged and cool mountainous terrain), the central midlands (rolling hills), and the western lowlands (coastal plains). Located at the extreme southern tip of the Indian subcontinent, Kerala lies near the center of the Indian tectonic plate (the Indian Plate); as such most of the state (notwithstanding isolated regions) is subjected to comparatively little seismic or volcanic activity. Geologically, pre-Cambrian and Pleistocene formations comprise the bulk of Kerala’s terrain. The topography consists of a hot and wet coastal plain gradually rising in elevation to the high hills and mountains of the Western Ghats. Kerala lies between north latitudes 8°.17'.30" N and 12°. 47'.40" N and east longitudes 74°.27'47" E and 77°.37'.12" E. Kerala’s climate is mainly wet and maritime tropical, heavily influenced by the seasonal heavy rains brought by the monsoon. The study mainly focused on Kerala coastal region wise are shown below, however only selected locations are considered for the present study which are discussed under subsequent chapters. Main Beach
Name of beaches Manjeshwar Beach Uppala Beach Shiriya Beach
Manjeshwar Beach
MoyalKoppalam Beach CPCRI Beach Cherangai Beach Kasaragod Beach
Bekal Beach
Kizhur Beach Chembirika Beach 11
Uduma Beach Kappil Beach Bekal Beach Pallikare Beach Kanhangad Beach Balla Beach Thaikadappuram Beach Nileswar Fish Landing Center Cresent Beach Valiyaparamba Beach Payyanur Beach
Ettikalam Beach Palkkod Beach Choottad Beach Azikkal Beach Meenunu Beach Payyabalam Beach Palliyamoola Beach Baby Beach Mappila Beach Kanam Beach
Mappila Beach
Adikaadalayi Beach Thottala Beach Kizhunna Beach Muzhappilangad Drive-In Beach Dharmadam Beach Chombala Harbor
Dharmadam Beach
aliyakal Beach Kolavi Beach Kolavipalam Beach
Payyoli Beach
Payyoli Beach 12
Thikkodi drive-in Beach UralathThazha Beach Kappad Beach Puthiyappa Fishing Harbour Kamburam Beach
Varakkal Beach Kamburam Beach Ancia Beach Beypore Beach KadalundiNagaram Beach
Parappanagadi Beach
Parappanagadi Beach Kettungal Beach Tanur Beach
Tirur Beach
Paravanna Beach Ayeekkal Beach Vakkad Beach Padinharekara Beach
Vakkad Beach
Ponnani Beach Periyambalam Beach Andathide Beach Mannalamkunnu Beach
Mannalamkunnu Beach
Panchavadi Beach Edakkaziyoor Beach Chengotta Beach Chavakkad Beach ManakkaKadav Beach Nakshathra Beach
Chavakkad Beach
Vadanappally Beach Snehatheram Beach Nattka Beach Valapadi Beach 13
Kazimbram Beach Palapetty Beach Koorikuzhi Beach Mooupedika Beach SreekrishnaMugam Beach Cherai Beach
Thattiukadava Beach Kara Beach Puthiya Beach Munnakai Beach Cherai Beach Ambekdker Beach Kuzhuppilly Beach
Vipin Beach
St: Ousep Beach Aniyal Beach Vipin Beach Nayarambalam Beach Puthuvayep Beach
Fort Kochin Beach
Manatna Beach Fort Kochin Beach Puthenthodu Beach
Cherthala Beach
Andhakaranazhi Beach Thaickal Beach Arthunkal Beach Thirivizha Beach Chethy Fishing Harbour Marrai Beach
Ambalapuzha Beach
Chettikad Beach Thumpoly Beach Alappuzha Beach Punnapara Beach 14
Ambalapuzha Beach Thottapally Fishing Harbuor Thottapally Beach Valiazhackal Beach Azheekal Beach Purayakkadavu Beach Arattupuzha Beach
Vellanathuruth Beach Neendakara Fishing Harbour Thrumullavaram Beach Thangassery Beach Kollam Beach Eravipuram Beach
Kollam Beach
Mayyanad Beach Chillakkal Beach ParavurThekkumbhagom Kappil Beach Varkala Beach Aaliyiakkam Beach MuthalaPozhiHarbour
Kandinamkulam Beach
Corocodile Beach Gloria Beach St. Androus Beach Vettuthura Beach
Veli Beach
Puthenthope Beach Pallithura Beach Vettukadu Beach Shangamugam Beach
Poonthura Beach
Valiathura Beach Grow Beach
Vizijam Beach
Hawa Beach 15
Light House Beach Vizijam Beach Vizijam Fishing Harbour Mullor Beach Poovar Beach
Poovar North Beach Poovar Golden Beach Poovar South Beach
Table.1.1 Kerala coastal areas (Google map)
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CHAPTER-II
LITERATURE REVIEW 2.1.
INTRODUCTION
The coastal zones have social and economic importance and are considered key climate change hot spots worldwide (IPCC, 2007a; Voice et al., 2006; EEA, 2010). The IPCC defines vulnerability as “The degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude, and rate of climate variation to which a system is exposed, its sensitivity, and its adaptive capacity” (IPCC, 2001b, Glossary).
Marine and Coastal EEA/EIONET workshop, Oct 2010 organized a first expert workshop on methods for assessing current and future coastal vulnerability to climate change to consider
alternative
assessment
approaches(Link:-
https://webgate.ec.europa.eu/maritimeforum/en/node/1410). The workshop discussed about Climate Change Adaptation, Integrated Maritime Policy and related Maritime Spatial Planning, Marine Strategy Framework Directive, Water Framework Directive, Floods Directive and Integrated Coastal Zone Management Policy.
Coastal Zone Management Authority and Institute, State of the coast report 2000-Belize highlight the trends in the condition and use of Belize’s coastal resources. The Coast Report contains articles focusing on the connection between unsound land development and the resulting increase in destruction following natural disasters. In addition, this case study focused on the suitability for development and categorizing areas as being highly suitable for development, moderately suitable, least suitable, not suitable for development and areas already developed (http://www.coastalzonebelize.org/wp). Dwight Neal (2008) as part of the vulnerability assessment valuated, Belize’s capacity to adapt to climate change under the four criteria: Governance, Economic, Social and Ecosystem.
17
Similarly Torresan, et al., (2012), presented a regional vulnerability assessment (RVA) methodology which was developed to analyze site-specific spatial information on coastal vulnerability to the envisaged effects of global climate change, in order to assist coastal communities in operational coastal management and conservation for the coastal area of the North Adriatic Sea (Italy). The main aim of RVA was to identify key vulnerable receptors (i.e. natural and human ecosystems) in the considered region and localize vulnerable hot spot areas which could be considered as homogeneous geographic sites for the definition of adaptation strategies. The application of the RVA methodology is based on a heterogeneous subset of bio-geophysical and socio-economic vulnerability indicators (e.g. coastal topography, geomorphology, presence and distribution of vegetation cover, location of artificial protection), which are a measure of the potential harm from a range of climaterelated impacts (e.g. sea level rise inundation, storm surge flooding, coastal erosion). Based on a system of numerical weights and scores, the RVA provides relative vulnerability maps that allow to prioritize more vulnerable areas and targets of different climate-related impacts in the examined region and to support the identification of suitable areas for human settlements, infrastructures and economic activities, providing a basis for coastal zoning and land use planning.
Nicholls et al., (2007) explained that coasts are experiencing the adverse consequences of hazards related to climate change and sea level rice and would be exposed to increasing risks, including coastal erosion, over coming decades. Their paper discussed about sustainability for coastal areas that depend upon a combination of adaptation and mitigation. It included an assessment of current sensitivity and vulnerability, the key changes that coastal systems might undergo in response to climate and sea-level change, including coasts and other socioeconomic aspects, the potential for adaptation, and the implications for sustainable development. Anticipated climate-related changes include: an accelerated rise in sea level of up to 0.6 m or more by 2100; a further rise in sea surface temperatures by up to 3°C; an intensification of tropical and extra-tropical cyclones; larger extreme waves and storm surges; altered precipitation/run-off; and ocean acidification. These phenomena would vary considerably at regional and local scales, but the impacts are virtually certain to be overwhelmingly negative (Nicholls et al., 2007). 18
There are other climate-related effects in coastal zones besides sea-level rise such as the change in the frequency, intensity and spatial patterns of coastal storms, changes in wave climate both regarding the average direction and intensity of the transported energy and changes in precipitation. Other climatic changes that could have significant consequences for coastal zones, such as changes in wind direction and intensity, remain highly uncertain. The coastline is constantly changing through the action of several factors such as wave height and direction, wind speed, water depth, sediment supply, removal and transport along the coast, strength of tides, rates of relative sea level change, rainfall and intensity of extreme meteorological and climate events. Furthermore, coastal ecosystems are also particularly sensitive to the increase in sea surface temperature, ocean acidification, salt water intrusion, rising water tables and to altered runoff patterns (ETC-ACC, 2010a).
Ramieriet al. (2011), pointed out those approaches and methods that might be concretely applied to derive coastal vulnerability maps or other summary information for the European and Regional Sea contexts. They recognized a need to understand what available methods (indicators, index, GIS and model based methods) can be operated and concretely applied for assessing coastal vulnerability to climate change. Ramieri et al. (2011) paper analyzing about European human socio-economic activities occur in coastal areas. Climate change adds additional pressure on European coastal systems thus increasing the vulnerability related to sea-level rise and other key meteorological changes. The main impacts of climate change in the coastal zone are expected to affect some countries like low-lying coastal areas, such as Denmark, the Netherlands, Italy, Germany and England. Coastal vulnerability in particular at the local or regional level, such as changes in hydrodynamic regimes, impacts on water trophic conditions, changes in biological communities and impacts on commercially important marine species.
Increasing seawater temperature can be especially important as this could affect the period of sea ice coverage, reducing coast’s ability to withstand wave impacts and erosion processes (Stre et al., 2003). Due to the salinity stratification of the Baltic Sea the rise of the sea level and possible changes in weather patterns could have many different types of effects, including changes in the fisheries (Hagen and Feistel, 2005). They described that the Black Sea is a highly anoxic body and restricted flushing makes it vulnerable to land-based 19
disturbances such as agricultural runoff, urbanization, and pollution (McCracken et al., 2008; Stanev, 2011). Changes in sea level, sea water pH and the extent of oxygen deficiency, together with other factors, can create negative synergistic effects to which Black Sea ecosystems (ETC-ACC, 2010a).coastal systems suffer great pressures from direct and indirect effects resulting from several human-induced drivers linked to population, economic growth, and related land-use changes. Thus coastal vulnerability assessments should adopt an integrated approach considering climate and non-climate induced environmental changes, socio-economic developments and the mutual interaction among these factors.
Smit et al., (2001); Nicholls and Klein, (2005) illustrated three basic adaptation strategies which were: • Protect - to reduce the risk of the event by decreasing the probability of its occurrence • Accommodate - to increase society’s ability to cope with the effects of the event • Retreat - to reduce the risk of the event by limiting its potential effects
2.2 Assessment types There are different methods of approaches for asserting vulnerability Index- and a workshop held in October 2010 based on ETC/ACC technical paper, 2010b identified several criteria for selecting and evaluating methods for assessing coastal vulnerability to climate change. Methods most commonly used to assess coastal vulnerability to climate change are:
Index-based methods -CVI
Indicator-based approach related GIS applications
GIS-based decision support systems (DSS) - DEYSCO and DITTY
Methods based on dynamic computer models- DIVA, SimCLIM, RegIS and Delft3D
2.3 Coastal vulnerability index (CVI) The Coastal Vulnerability Index (CVI) is one of the most commonly used and simple methods to assess coastal vulnerability to sea level rise, in particular due to erosion and/or 20
inundation (Gornitz et al., 1991). In most of the vulnerability assessment studies, the Coastal Vulnerability Index (CVI) and its adaptations are the most used techniques (Klein and Nicholls 1999). The CVI approach combines the coastal system susceptibility to change with its ability to adapt to changing environmental conditions and yields a relative measure of the system’s natural vulnerability to the effect of hazards as chronic and storm related coastal erosion processes and climate change associated processes. The application of CVI under a GIS environment or multivariate analysis was based on the modeling of a certain number of variables related to the specific hazard analyzed and existing risk assessment methods. From this the most sensitive areas can be easily identified by generating a set of color-coded sensitive maps (Gornitz 1990; LOICZ 1995; Bush et al. 1996; Cooper and McLaughlin 1998; Ojeda-Zújar et al. 2009; Raji et al. 2013).
Vivien et al. (1994) conducted a study of Coastal Risk Assessment Database developed for use with a geographic information system to identify the coastal areas at risk to erosion, permanent inundation, and episodic flooding in the U.S. Southeast coast. This database contains 13 land, marine, and climatological variables, including: lithology, elevation, subsidence, erosion/accretion, tropical storm probabilities of occurrence, and maximum storm surge. These variables were grouped into three categories using factor analysis. Each category was then weighted based on its perceived importance in determining the relative risk of an area to erosion or inundation. These weighted factors were used to calculate a risk index. The coastal vulnerability linked to natural hazards in northern Brazil, introduced socioeconomic parameters as the total population and total population affected by floods, density of population, nonlocal population, poverty and municipal prosperity (Szlafsztein and Sterr (2007)). In a study carried out in China by Li and Li (2011) Social economic index (e.g. population, roads, industrial and agricultural value and residential land), Land use index (farming, aquaculture and arable land), Eco-environmental index (beaches and wetlands, mangroves and rivers), Coastal construction index (coastal engineering, highways and buildings), Disaster-bearing capability index (seawalls, labor population and financial revenue) were considered.
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The formula were proposed and tested for the derivation of a Coastal Vulnerability Index (CVI) in Gornitz et al. (1991); CVI5 was used in Gornitz and White (1991), Gornitz et al. (1991), and Gornitz (1990,1991). Coastal vulnerability index (CVI) formulation includes 6 or 7 variables. It described as,
Product Mean
CV1 = (X1*X2*X3*X4*…..Xn) n
Modified Product mean
CV2= [X1*X2*1/2 X3+X4*X5*1/2(X6&X7)] n-2
Where n= variable present
X1=Mean deviation
X2=local subsidence
X3=geology
X4=geomorphology
X5=mean shoreline displacement
X6=maximum wave height
X7=mean tidal range
Table 2.1 Coastal vulnerability index (CVI) formula The U.S. Geological Survey (USGS) used this formulation to evaluate the potential vulnerability of the U.S. coastline at the national scale (Thieler and Hammar-Klose, 2000) and on a more detailed scale for the U.S. National Park Service (Thieler et al., 2002). Elizabeth et. al.(2004) determined the vulnerability index by focusing on six variables that strongly influenced coastal evolution: 1) Geomorphology 2) Historical shoreline change rate 3) Regional coastal slope 4) Relative sea-level change 5) Mean significant wave height 6) Mean tidal range. USGS has considered six variables, combined through the following equation:
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Where: a = geomorphology; b = shoreline change rates; c = coastal slope; d = relative sea level rate; e =mean significant wave height; f = mean tidal range.
Ojeda-Zújar et al. (2009) applied the same CVI formulation to the Andalusia coastline ranking and then results of the analysis had been mapped through a GISsystem, thus enabling the identification of the most vulnerable areas. Other authors slightly adapted the CVI to a particular coastal zone or region, and modified the formula as
Where: a1 = dune height; a2 = barrier type; a3 = beach type; a4 = relative sea-level change; a5 = shoreline erosion or accretion; a6 = mean tidal range; a7 = mean wave height
Their study explained ranking of Australian beaches, ranking based on key variables like Dune height, Barrier types, Beach types, Relative sea-level change, Shoreline erosion accretion, Mean tidal range, Mean wave height.. Ozyurt et al. (2008), Ozyurt and Ergin (2009, 2010) and Ergin (2011) proposed an assessment method to determine the associated vulnerability to Sea level Rise for different coastal areas in Turkey. Ozyurt (2007) and Ozyurt et al. (2008) developed a CVI to specifically assess impacts induced by sea level rise. The index was determined through the integration of 5 sub-indices, each one corresponding to a specific sea level rise related impact. The author applied this methodology to the Goksu Delta in Turkey, where the five considered SLR impacts were: coastal erosion, flooding due to storm surges, permanent inundation, saltwater intrusion to groundwater resources and salt water intrusion to rivers/estuaries. Each sub-index was determined by the semi-quantitative assessment of both physical and human influence parameters.
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PP = Physical Parameters; HP = Human Influence Parameters; n and m = the number of physical and human influence parameters respectively.
Human influenced Parameters are reduction of sediment supply, river flow regulation, engineered frontage, Groundwater consumption, land use pattern, natural protection degradation, and coastal protection structures. Physical Parameters are rate of SLR, Geomorphology, coastal slope, significant wave height, sediment budget, tidal range, proximity to coast, type of aquifer, hydraulic conductivity, depth to groundwater level above sea, river discharge, water depth at downstream. Szlafsztein and Sterr (2007) formulated an index combining a number of separate variables that reflect natural and socio-economic characteristics that contribute to coastal vulnerability due to natural hazards. The classification of all the coastal information had been greatly aided by the development of GIS applications as well as integrated remote sensing applications. Separated GIS-layers were overlaid and the variable scores combined into natural and socio-economic vulnerability indices. McLaughlin and Cooper (2010) and (McLaughlin et. al., 2002) took socio-economic data in assessing coastal vulnerability indices: constraints and opportunities a multi-scale CVI, specifically integrating erosion impacts, which can be applied to climate change induced impact studies. The index integrates three sub-indices: (i) a coastal characteristic sub-index, describing the resilience and coastal susceptibility to erosion, (ii) a coastal forcing sub-index, characterizing the forcing variables contributing to wave-induced erosion, (iii) a socioeconomic sub-index, describing targets potentially at risk.
2.4 Indicator-based approach Indicator-based approach on the basis of the Drivers-Pressure-State-Impact-Response (DPSIR), a causal framework for describing the interactions between society and the environment. This framework has been adopted by the European Environment Agency) 24
(EEA, 1995) the erosion project identified thirteen indicators to support the assessment of coastal erosion risk throughout Europe (Table 2.2). The indicator set included nine sensitivity indicators: 1) Relative sea level rise 2) Shoreline evolution trend status 3) Shoreline changes from stability to erosion or accretion 4) Highest water level 5) Coastal urbanization (in the 10 km land strip) 6) Reduction of river sediment supply 7) Geological coastal type 8) Elevation 9) Engineered frontage. Four impact indicators were identified: •
Population living within the RICE (Radius of influence of coastal erosion and flooding);
•
Coastal urbanization (in the 10 km land strip);
•
Urbanized and industrial areas within the RICE;
•
Areas of high ecological value within the RICE.
Goals 1. To control further development of the undeveloped coast as appropriate
Indicators 1) Demand for property on the coast 2) Area of built-up land 3) Rate of development of previously undeveloped land 4) Demand for road travel on the coast 5) Pressure for coastal and marine recreation 6) Land taken up by intensive agriculture
2. To protect, enhance and
7) Amount of semi-natural habitat 25
celebrate natural and cultural diversity
8) Area of land and sea protected by statutory designations 9) Effective management of designated sites 10) Change in significance coastal and marine habitats and species
3. To promote and support a dynamic and sustainable coastal economy
11) Loss of cultural distinctiveness 12) Patterns of sectorial employment 13) Volume of port traffic 14) Intensity of tourism 15) Sustainable tourism
4. To ensure that beaches are clean and that coastal waters are unpolluted
16) Quality of bathing water 17) Amount of coastal, estuarine and marine litter 18) Concentration of nutrients in coastal waters
5. To reduce social exclusion and promote social cohesion in coastal communities
19) Amount of oil pollution 20) Degree of social cohesion 21) Relative household prosperity 22) Second and holiday homes
6. To use natural resources wisely
23) Fish stocks and fish landings 24) Water consumption
7. To recognize the threat to coastal zones posed by climate change and to ensure appropriate and ecologically responsible coastal protection
25) Sea level rise and extreme weather conditions 26) Coastal erosion and accretion 27) Natural, human and economic assets at risk
Table 2.2 Indicator based approach adopted by the European Environment Agency
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2.5 GIS-based decision support systems (DSS) - DEYSCO and DITTY Deduce sustainable development indicators (source: Deduce Consortium, 2007) gives an overall idea about GIS-based Decision Support Systems like DITTY-DSS (Development of an information technology tool- Decision Support System) and DESYCO (DESYCO is an open source software that manages different input data, a GIS-based Decision Support System (DSS) (Fig 2.1) aimed at the integrated assessment of multiple climate change impacts on vulnerable coastal systems). DESYCO requires the analysis of different climate change related stressors (e.g. sea level rise, storm surges, waves, water temperature and salinity) and affected resources (e.g. water, soil, biodiversity) in order to assist coastal communities in planning adaptation measures. The DESYCO overall implementation is composed of three main phases: • The scenarios construction, aimed at the definition of future climate scenarios for the examined case study area at the regional scale • The integrated impact-risk assessment, aimed at the prioritization of impacts, targets and affected areas at the regional scale • The impact-risk management, devoted to support adaptation strategies for the reduction of the risks and impacts in the coastal zone, according to ICZM principles. According to the Rwanda Revenue Authority(RRA) vulnerability indicators are classified in three main categories of factors, they are: • Susceptibility Factors (SFs), describing the degree to which a receptor is affected by climate related stimuli, • Value Factors (VFs), identifying relevant environmental and socio-economic values of the receptors that need to be preserved for the interest of the community (e.g. land use, human activities); • Pathway Factors (PFs), being physical characteristics of the receptors determining their exposure to climate change hazards (e.g. elevation, distance from coastline).
Within DESYCO and the related RRA approach, numerical model simulations used for the construction of climate change scenarios and exposure maps have been validated through the comparison with observed data for a control period DITTY-DSS. As highlighted previously, flexibility is a key factor in vulnerability assessment tools. 27
External Factors Control options
Data Storage
Models
Multi criteria Analysis
Decision
Figure 2.1 Block scheme of the DITTY-DSS
2.6 Methods based on dynamic computer models- DIVA, SimCLIM, RegIS and Delft3D Risk Assessment of Coastal Erosion DIVA, SimCLIM, RegISand Delft3D are dynamic computer model methods. The DIVA tool is an integrated, global model of coastal systems that
assesses
biophysical
and
socio-economic
consequences
of
sea-level
rise.
The impacts such as coastal erosion, coastal flooding wetland change and salinity intrusion into deltas and estuaries can be assessed using this tool. DIVA also enables to take in consideration, within the assessment adaptation, in terms of raising dikes and nourishing beaches while a predefined adaptation strategies are used. The first version of DIVA was developed within the EC-funded project DINAS-COAST (Dynamic and Interactive Assessment of National, Regional and Global Vulnerability of Coastal Zones to Climate Change and Sea-Level Rise). Afterward DIVA has been progressively developed and used in different application. DIVA is currently not available for download due to a lack of resources for maintaining and supporting the software (ETC-ACC, 2010b).
SimCLIM computer model system used for examined the effects of climate variability and change over time and space. SimCLIM based on an "open-framework" feature that allows users to customize the model for their own geographical area and spatial resolution and to attach impact models. This supports to decision making and climate proofing in a wide range of situations where climate and climate change pose risk and uncertainty. Vulnerability can be assessed both currently and in the future. Adaptation measures can be tested for present day conditions and under future scenarios of climate change and variability. With the program, users can conduct sensitivity analysis and examine sector impacts of climate change. SimCLIM can be applied from local to global scales and it includes a sea-level 28
scenario generator which allows the inclusion of regional and local parameters linked to the coastal areas and a simulation model of shoreline changes for beach and dune systems. The RegIS project (Regional Climate Change Impact and Response Studies in East Anglia and North West England) was a first attempt to quantitatively model the cross-sectorial impacts of climate and socio-economic change within an integrated framework at a regional scale within the UK. The integrated methodology followed a Drivers-Pressure-State-ImpactResponse (DPSIR) framework, and considered impacts on coastal areas, river flooding, agriculture, water resources, and biodiversity. The project also developed a software tool (the Regional Impact Simulator; RegIS tool) for use by policy makers to analyses the interactions between impacts with differing scenarios of socioeconomic development, and different future climates. The tool also allows the user to generate an integrated assessment of the effects of different adaptation strategies. The software contains a suite of computer models within a user friendly interface that allows the user to: (i) rapidly identify the sensitivity of an indicator to climate change and/or socio-economic change, (ii) investigate the effects of uncertainty in the future scenario, (iii)investigate regional adaptive responses to future change
Delft3D is a 2D/3D modeling suite to investigate hydrodynamics, sediment transport, morphological dynamic and water quality for fluvial, estuarine and coastal environments. The software is used for many applications around the world, including Netherlands, USA, Hong Kong, Singapore, Australia, and Venice. The software is continuously improved and developed with innovative advanced modeling techniques as consequence of there search work of the developing institute. It is an open-source model composed of a number of modules, each addressing a specific domain of interest, such as: flow, near-field and far-field water quality, wave generation and propagation, morphology and sediment transport, together with preprocessing and post-processing modules. All modules are dynamically interfaced to exchange data and results. At the level of local to regional planning, web-based tools can provide planners with detailed information on different aspects of coastal zone management.
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Wide variety of coastal assessment methods were described in this technical paper. Indicators and index-based methods are providing useful tools for first look assessment, thus supporting identification of priority vulnerable areas. Sector models enable detailed quantitative analyses of coastal processes. Integrated assessment models can evaluate the vulnerability of coastal systems to multiple climate change impacts. The selection of an assessment method to be applied in a particular context is also strongly dependent on availability of relevant data .Socio-economic system have most important role in Coastal vulnerability assessment. Indeed pressures of socio-economic activity may even generate more severe effects than those from climate change and sea level rise. The paper gives clear idea about the consideration of existing and planned adaptation strategies. The realistic simulation of adaptation is complex, and human decisions are not fully predictable. Sustainability requires that ecological needs are also taken in consideration. For example, hard protection of coastal infrastructure can protect human settlements and infrastructure against erosion or flooding but may be counterproductive for ecological processes and ecosystem dynamics.
2.7 Coastal Vulnerability Assessment and Mapping- A review of real life application Methods for assessing coastal vulnerability to climate change by Ramieri et al. (2011), provided a wide variety of methods for the assessment of coastal vulnerability to climate change, which differ in scope, approach, complexity and application scale. The different data that are sea level rise, land subsidence, projection of other climate change drivers, topographic and bathymetric data, soil characteristics, socio-economic data used for appropriate assessment method. The study has demonstrated that the coast of Aaccra is vulnerable to climate change and its associated sea level rise. The risk level for the entire coastal area can be categorized as moderate. The most vulnerable area is the western section where sea level rise will result in increased erosion and inundation of the low lying areas, especially the Densu wetlands area. In order to develop a database for a local scale assessment of coastal vulnerability for the Accara coast, relevant data were gathered from various sources. The sources include government agencies such as the geological survey 30
department and the survey and mapping division of the Ghana lands commission as well as journal articles. Assessment provide with the help of relative risk factors for elevation, shoreline displacement, local subsidence trend, tidal range, and wave height, risk factors for geology, risk factors for geomorphology, risk factors for identified variables in the three geomorphic sections. New Jersey Department of Environmental Protection (2011) explains, New Jersey is truly a coastal state, encompassing 127 miles of Atlantic coastline and nearly 1,800 miles of estuarine shoreline. The coastal zone is comprised of multiple shoreline types, including spits, headlands, high energy barrier island systems, mixed energy barrier island systems, and bays, all of which sustain habitat and wildlife, support viable maritime and tourism industries, and harbor a way of life for many New Jersey residents. Approximately 1.8 million people live within the coastal counties of New Jersey Unfortunately; much of New Jersey’s coastal landscape is threatened by the impacts of episodic and chronic erosion, subsidence, shallow coastal flooding, tropical storms and hurricanes.
Coastal communities have merely responded to the impacts of natural hazards. In recent decades, the federal government has proactively attempted to protect people and property from natural disasters by improving coastal construction standards, developing incentives to reduce flood losses, and requiring mitigation planning in order for local governments to obtain pre and post disaster mitigation funding. New Jersey has further strengthened floodplain construction standards by requiring all new development to be constructed two feet above base flood elevation. Despite federal and state efforts, coastal development continues to be sited in high hazard areas, increasing property damage losses and recovery costs for coastal communities. The New Jersey Office of Coastal Management (NJOCM) incorporated elements from numerous tools, guidebooks, and training courses to develop two assessment tools to ensure that coastal communities have consistent and comprehensive guidance to assess their vulnerability and capacity for resilience. The Coastal Community Vulnerability Assessment and Mapping Protocol (CCVAMP) was developed to guide communities through the development of a geospatial vulnerability assessment, while the Getting to Resilience. 31
Reyes & Blanco (2012), has verified the potential risks to future sea level rise of the coastal communities located in the three barangays. The susceptibility of these communities to the possible effects of sea level rise extends to their properties and livelihood. The merged data obtained from several altimetry missions provide an effective scheme of analysing sea level variations over a long period of time. The case study of the North Adriatic Sea by Torresan, (2015), shows the impacts on Soil and Coast. This paper provide application of the regional vulnerability assessment methodology to the coastal area of the North Adriatic Sealed to a ranking of the relative vulnerability of each analysed receptor (i.e. Beaches, river mouths, wetlands, terrestrial biological systems, protected areas, urban and agricultural areas) in relation to potential climate change impacts (i.e. Sea level rise inundation, storm surge flooding and coastal erosion). The procedure proposed for the regional vulnerability assessment can effectively support decision makers in the spatial identification of the areas and targets characterized by different vulnerability levels and in the definition of management options useful to preserve the coastal receptors, which are potentially impacted by climate-related hazards at the regional scale. In this study final vulnerability rankings are unit less numbers that judge the relative degree of receptor vulnerability to each analysed impact, in relation to qualitative vulnerability classes (i.e. Very high, high, medium, low, very low). Consequently, higher vulnerability values do not imply high vulnerability in absolute terms, but only compared to other case study receptors and sub-areas for a given impact. The final decision-making process should therefore consider not only the final values of the index, but also the factors that contributed in determining that final vulnerability value (i.e. Susceptibility, pathway or value factors). A correct interpretation of these factors is particularly relevant for the analysis of the adaptation measures that could be suitable for reducing the vulnerability. Jochen et. al.(2009), Integrating knowledge to assess coastal vulnerability to sea-level rise has argued that the methodological advancement of vulnerability assessment would be from the development of two elements:(i)a domain-independent conceptual framework of vulnerability to enable unambiguous communication about vulnerability and meaningful comparison between vulnerability assessments and (ii) process to organise the specialisation of the framework's general concepts in to operational, system-specific definitions so as to facilitate the integration of knowledge from different experts and disciplines. The generic nature of both the formal framework and the diva method makes them easily extensible and 32
transferable to address new challenges. Improve the version of the diva model could include develop a module for coral reefs and atolls and they consider consequences of climate change other than sea-level Rise (including extreme events),focusing more strongly on river-coast interactions, refining. The adaptation module and increasing the spatial resolution of the model, thus increasing DIVA’s usefulness to coastal management. Ponsy and Sumam (2013), described a study that facilitated in the identification of the vulnerable regions along the Chavakkad coast. They analysed the regions by using remotely sensed data along with the conventional data. The coastal vulnerability maps produced using this technique serve as a broad indicator of threats, to the people living in coastal zones.
2.8 Analytical hierarchy process (AHP) for the fuzzy vulnerability assessment model “The Analytic Hierarchy Process (AHP) is a method that can be used to establish measures in both the physical (objective reality outside the individual) and social domains (subjective ideas, feelings, and beliefs of an individual, of a group working together)” AHP is a hierarchical representation of a system. A hierarchy is an abstraction of the structure of the system, consisting of several levels representing the decomposition of the overall objective to a set of clusters, sub down to the final level” - (Cheng and Li 2001). AHP combines both qualitative and quantitative approaches into a single empirical inquiry. AHP uses a qualitative way to decompose an unstructured problem into a systematic decision hierarchy quantitative sense, it employs a pair wise comparison to execute the consistency test to validate the consistency of responses. AHP aims at assigning weights to tested elements and weighting of elements has two major functions; 1. Prioritize determine the key elements, 2.To assign weights to key measures to make more accurate decisions. This project used fuzzy logic to make decision towards the coastal vulnerability and its impacts in Kerala coast. Most decisions that people make are logically, we look at the situation and make a decision based on the situation. Fuzzy Decision Making employs fuzzy technologies to obtain optimal solutions and support in making the best possible decisions. Fuzzy Decision Making provides advancing practice in the presence of uncertainty. The use of AHP for the fuzzy vulnerability assessment model is described step by step in following section. 33
2.9 Making Fuzzy Decisions, problem statement Problems that the decision makers of coastal zone authorities facing is lack of coastal zone management practices due to the outcomes of decisions regarding the complex physical processes that not happen as expected. All uncertainties and conflicts that are present at coastal areas, impacts of climate change that associated with high uncertainty, turn decision making into a risky process. To prioritize coastal areas according to their vulnerability to sea level rise is one of the objectives of the fuzzy vulnerability assessment model that requires ranking of coastal areas which help decision makers to actual policy making processes (adaptation options). This project present a fuzzy vulnerability assessment model to prioritize coastal areas of Kerala according to their vulnerability to sea level rise. Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0), a Boolean logic on which the modern computer is based. The idea of fuzzy logic was first advanced by LotfiZadeh and Zadeh(1975).
To prioritize coastal areas of Kerala according to their vulnerability to sea level rise, the objectives of the fuzzy vulnerability assessment model requires ranking of coastal areas. The comparison of individual impacts of sea level rise according to vulnerability as well as selecting the governing parameters for site specific vulnerability requires ranking of impacts and parameters relatively. Same time, the physical impacts are complex and continuous processes, which different set of criteria and sub-criteria are necessary to be defined. Not all the parameters have equal influence on the physical process assessed by the model. To derive most realistic results using different sets of criteria requires assignment of weights to different parameters and criteria with respect to their influence on the impact vulnerability. The objective of integrating physical parameters and human influence parameters as well as the problem structure which is very suitable for hierarchical definition enables the use of AHP to assign weight to the criteria. Here Fuzzy logic used take decision towards the coastal vulnerability and its impacts in Kerala coast by choosing parameters that cause coastal vulnerability and find out how much affect these parameters to each coastal area by proper analyses.
34
For example:If P, then Q-P Therefore, Q. This form of logical reasoning is fairly strict, Q can only be if P. Fuzzy logic loosens this strictness by saying that Q can mostly be if P is mostly. P and Q are now fuzzy numbers. If P, then Q-mostly P Therefore, mostly Q. The general rule can described as, "if x is A then y is B," where x and y are fuzzy numbers in the fuzzy sets A and B respectively. These fuzzy sets are defined by membership functions. There can be any number of input and output membership functions for the same input as well, depending on the number of rules in the system. A set of rules that have one input in a fuzzy set and one output in a fuzzy set: If x is Ai then y is Bi, i=1,2,...n, a system that has two input membership functions (A1,A2) and two output membership functions (B1, B2). The fuzzy decision making described by following inputs and corresponding output based on the coastal vulnerability and resistance. Two factors are considered here such as adaptation and sea level rise.
Intensity of Resistance Problem Rules-adaptation Factor • If adaptation is bad, then Resistance is defective • If adaptation is average, then Resistance is standard • If adaptation is good, then Resistance is high-quality
Intensity of Resistance Problem Rules-Sea Level Rise Factor •
If Sea Level Rise is low, then Resistance is high-quality
•
If Sea Level Rise is average, then Resistance is standard If Sea Level Rise is high, then Resistance is defective combine the two different lists of rules into following method,
•
If adaptation is bad and Sea Level Rise is high, then Resistance is defective
•
If adaptation is average and Sea Level Rise is average, then Resistance is standard
•
If adaptation is good and Sea Level Rise is low, then Resistance is high-quality By using these inputs, fuzzy logic can assign a set of the output, thus it can conclude and
design a model in fuzzy logic system. It provides ranking facility of coastal vulnerability to 35
each coastal areas. With the help of these ranking authorities can take immediate preventive measures to the higher vulnerable regions.
2.10 Existing fuzzy logic decision making system The scientific study of Ayman (2012) on Nile delta gave overall idea about how to implement fuzzy logic decision making system towards vulnerable sea level rise. The paper described about Egypt, considered as one of the top five countries expected to be mostly impacted with sea level rise due to low elevation in the Nile delta region. Egypt face environmental crises such as shore erosion, salt-water intrusion and soil salinity. They proposed multi-criterion analysis for effective ranking and take adaptation strategy by using fuzzy decision making technique. The decision-making process involves determining the set of alternatives, evaluating alternatives and comparison between alternatives. The model is suitable in evaluating situations that deal with ambiguity and vagueness, involve subjectivity, and imprecise information. Using fuzzy decision making technique, enables maximum benefit from practical know-how, take into account several variables and perform “weighted merging” of influencing into variables. Fuzzy Decision Making employs fuzzy technologies to obtain optimal solutions and support in making the best possible decisions. Fuzzy Decision Making provides advancing practice in the presence of uncertainty. In this contribution, the goal is to help foster the understanding, development, and practice of fuzzy technologies for solving resilience to affected regions due to climate change problems.
Fuzzy logic is a form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their contexts. Fuzzy logic provides a method to formalize reasoning when dealing with vague terms. Fuzzy logic provides an alternative way to represent linguistic and subjective attributes of the real world in computing. Traditional computing requires finite precision which is not always possible in real world scenarios. Not every decision is either true or false, or as with Boolean logic either 0 or 1. Fuzzy logic allows for membership functions, or degrees of truthfulness and falsehoods. Or as with Boolean logic, not only 0 and 1 but all the numbers that fall in between. 36
Analyzing flood vulnerable areas with multi criteria evaluation by Yalcin and Akyurek (2013) described the Multi criteria Evaluation (MCE) methods which are used to analyses the flood vulnerable areas. They described about Geographical Information Systems (GIS) to provide more flexible and more accurate data to the decision makers in order to evaluate the effective factors. Some of the causative factors for flooding in watershed were taken into account as annual rainfall, size of watershed, basin slope, gradient of main drainage channel, drainage density, land use and the type of soil. In this study two main MCE approaches employed in GIS are used, namely Boolean and Weighted Linear Combination (WLC). In MCE, two methods, namely Ranking Method and Pairwise Comparison Method, are used to calculate the best decision. The standardized values of the factors are considered as a fuzzy measure concept expressed as fuzzy set membership. Analyzed the data and applied in to the fuzzy logic system.
Decision making is a choice or selection of alternative course of action in many fields, both the social and natural sciences. The unavoidable problems in these fields necessitate detailed analysis considering a large number of different criteria. All these criteria need to be evaluated for decision analysis. In a classification based on Boolean Logic, an area is either accepted or rejected based on a given threshold value. Besides the problems associated with the use of Boolean Logic, multi-criteria evaluation (MCE) methods have been applied. Since 80 per cent of data used by decision makers is related geographically (Malczewski, 1999a), Geographical Information System (GIS) may provide more and better information about decision making situations. GIS allows the decision maker to identify a list meeting a predefined set of criteria with the overlay process (Heywood et al., 1995).
The first step in assessing the vulnerability structure is to determine the factors affecting the flood on the basis of an analysis of existing studies and knowledge. Here, judgements made by experts on hydrology and hydraulics can be applied. These factors are used as criterion separately. A criterion is a basis for a decision that can be measured and evaluated (Eastman et al., 1995). Layers representing the criteria are referred to as criterion maps. They selected the Study Area West of Black Sea in the north of Turkey and collect the data that’s belongs to vulnerability by using GIS. Thus they mentioned the vulnerable criteria and 37
made a map by using the same and applied into Boolean logic same as fuzzy and then a high to low vulnerability ranking technique is applied with fuzzy logic concept.
Westmacott (2001) in his paper revealed importance of coastal zone management and the components of the decision making environment and the components of a decision support system. It also explored the various techniques available to deal with different modelling needs, the constraints of inadequate data and the multi-objective decision making environment. Integrated coastal management in the tropics requires the conservation of vulnerable and diverse ecosystems such as coral reefs and mangroves as well as the management of land and marine-based human activities. Decision-making for integrated coastal management involves multiple decision-makers and multiple stakeholders often with conflicting needs and interests. Decision support systems can be developed to improve our understanding of the inter-relationships between the natural and socio-economic variables and hence result in improved decision-making. The question is whether this decision making environment is actually too complex for the development of useful and useable decision support systems. This paper described the components of the decision making environment and the components of a decision support system. It also explored the various techniques available to deal with different modeling needs, the constraints of inadequate data and the multi-objective decision making environment. Different techniques of developing decision support systems can play important roles within integrated coastal management.
Geraghty, (1993) described about the fuzzy model. Human expert tackles real-world problems using a set of rules, heuristics and inferences programmed into a computer system. These systems interpret information and reason toward a conclusion with the aim of obtaining the same results that the human expert would arrive at if presented with a comparable task expert systems and fuzzy reasoning based on fuzzy sets (Zadeh, 1965) are able to convert qualitative reasoning into quantitative mathematics (Kainuma et al., 1990,1991).
The power of expert systems is that they are potentially capable of providing expertise where the expert is not available and as a result are able to build capacity (Geraghty, 1993). Fuzzy modelling, first introduced by Zadeh (1965), is based on the concept that things are 38
not true or false, black or white but rather shades of grey (Kosko, 1994). It utilises qualitative, linguistic relationships based on expert knowledge and judgement to link a set of inputs to a set of outputs. This type of modelling can be implemented where sufficient numerical data to build up a statistical model using traditional methods may be missing (Kainuma et al., 1991; Nijkamp and van den Burgh, 1997). Fuzzy logic has been applied to many different applications relating to ICM. Examples include modelling the anthropogenic impacts on coral reefs (Meesters et al., 1998), modelling the impact of recreation on the environment in the Western Scheldt (van der Werff and Goosens, 1997), selecting alternative projects when environmental evaluation is vague or little understood (Smith, 1994), classifying vegetation classes from remotely sensed images (Foody, 1996) and even modeling the hydrological cycle (Bardossy, 1996). Fuzzy logic provides a means of incorporating those traditionally non-quantifiable goals into the decision-making process.
The scientific paper A new methodology for evaluating coastal scenery: fuzzy logic systems by Ergin et. Cal. (Civil Engineering Department, Middle East Technical University, Ankara, Turkey ICoD, Foundation for International Studies, University of Malta, Valletta, Malta Applied Sciences Department, University of Glamorgan, Pontypridd, Wales, UK) contribute best methodology for coastal evaluation. The paper describes Coastal structures evaluated by utilization of selected landscape components was subjected to a fuzzy logic systems approach. Twenty-six top-rated parameters were identified from a literature search/questionnaire surveys carried out in Malta, Turkey and the UK and coastal scenery investigated at 57 sites. In this paper revel the importance of coastal tourism. So coastal scenic evaluation is consideration for aerial comparison purposes as coastal landscape is a very important resource. Scenery is part of a coastal landscape inventory available for managers or planners for coastal preservation, protection, development etc. In evaluating coastal scenery, this paper utilized a checklist approach, a methodology that is popular in many facets of both the natural and socioeconomic disciplines. Checklist plus a fuzzy logic systems approach. A fuzzy logic approach enables an expert group to quantify the uncertainties and subjective pronouncements inherent in most scientific studies (Ambala 2001), the paper evaluated characteristics or parameters used to assess a certain region and be measurable (cliff height, shore width, etc.), many others are experts’ view of the coastal scenery and are given using terms ‘good’ or ‘bad’; ‘clean’ or ‘not clean’, etc. And also 39
consider concepts based upon experience, intuition, human nature, environmental conditions, national cultural and social policies and economic conditions. Fuzzy Logic Approach (FLA) is a tool to assess the possibility (magnitude) and the degree of each factor considered to affect the evaluation results. Zadeh (1965) proposed making the membership function (or the values True and False) operate over the range of real numbers in the interval [0.0, 1.0] instead of on 0 and 1 of classic Boolean logic. This implies that fuzzy logic may allow more than one conclusion per rule. This study aims to comprehensively assess the dominance of physical and human, in coastal scenery evaluation. Therefore it is an appropriate study in which to use fuzzy logic mathematics. For the sake of simplicity in mathematical and numerical processing, a condensed version of fuzzy analysis was adapted for the decisionmaking phase of the coastal scenery investigated. The scenic assessment factor set F is defined as composed of physical (P) and human (H) factors. F is expressed as: F = (Physical, Human) = (P, H) •
P = cliff, beach, rocky shore, dunes, valley, land form, tides, coastal landscape features, vistas, water color and clarity, natural vegetation cover, vegetation debris.
•
H = noise, litter, sewage, non-built environment, built environment, access type, skyline, utilities
Coastal scenic assessments of the sites and scenic evaluation carried out by the fuzzy methodology were presented by: 1.
Scenic Evaluation Score Histograms
2.
Fuzzy Weighted Average Matrices of Physical and Human Factors
3.
Membership Degrees of Physical and Human Factors
Site classifications were made on the final sequence curve produced, based on the Evaluation Index D. Curve break points based on the midpoint change of slope allowed a division of sites into five main classes. They are; Class 1: Extremely attractive natural site with a very high landscape value, having a D-value above 0.85, e.g. Cirali and Phasalis Bay, Turkey (Plate 1).
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Class 2: Attractive natural site with high landscape value, having a D-value between 0.65 and 0.85, e.g. St Govans, UK and Tisan Back Bay, Turkey (Plate 2). Class 3: Mainly natural with little outstanding landscape features and a D-value between 0.4 and 0.65, e.g. Manikata, Malta; Southerndown Bay, UK (Plate 3); or urban sites with exceptional scenic characteristics, e.g. Tenby, UK Class 4: Mainly unattractive urban, with a low landscape value and a D-value between 0 and 0.4, e.g. Xlendi Bay, Gozo, Malta (Plate 4). Class 5: Very unattractive urban, intensive development with a low landscape value and a Dvalue below 0, e.g. Kizkalesi, Turkey and St Georges Bay, Malta (Plate 5). An innovative coastal scenic evaluation (CSE) technique was developed in this paper. The paper give more information to my project based on the following points. • Eighteen physical and eight human parameters, sub-divided into a five-scale attribute rating system. • A weighting index derived from a perception study, which ranked parameter importance. • A fuzzy logic approach utilizing a mathematical model. • Management tools reflecting strengths and weaknesses of evaluated sites based on data presentation in the form of assessment histograms and weighted averages versus attributes. The latter reflects the effect of physical and human parameters on a scenic assessment, which can be used as a tool by coastal planners. • Membership degree vs attribute curves, for identification of the most appropriate D (Evaluation index) criteria. The skew of the membership degree vs attribute curve reflects the scenic value of assessed sites. • A Coastal Scenic Classification Curve, determined for all 57 evaluated sites based upon calculated Evaluation index values (D). The latter reflected the importance of attribute values in terms of weighted areas. • A five-class evaluation system for coastal scenery was developed.
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CHAPTER –III Data and Methodology 3.1 Data Collection Data are obtained by questioning experts who are actively involved in coastal engineering and coastal zone management research and also from literature. •
Satellite data(USGS)
The general public has access to the most up-to-date information of the environmental coastal conditions by looking at the satellite maps. Software using, RG-IS is the Resource Geographic Information System involve wide variety of geospatial data. •
Data of sea level rise
The tide gauge data set of Global Sea Level Observing System (GLOSS) was used as the primary source of information for sea level trend in the study area. •
Tidal range
Tidal range values were obtained in the tide tables from Harbor Engineering Department, Government of Kerala •
Data of Slope and elevation
Shuttle Radar Topography Mission (SRTM) Digital Elevation Model had been used to generate the coastal slope and elevation. •
Data of Road network
Road network from the Google Earth images •
Data of Shoreline change rate
Calculate the shoreline change rate using DSAS tool of Arc-GIS.
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•
Hydrological data
Data used for study of the movement, distribution, and quality of water on Earth and other planets, including the hydrologic cycle, water resources and environmental watershed sustainability. Hydrological data is help to view the information on water levels and related weather conditions. •
Metrological Data(IMD) Data used for study of the atmosphere that explains and predicts atmospheric phenomena.
IMD provides weather reports on specific weather phenomenon (heavy rainfall, storm etc.) of Kerala regions. •
Coastal land use data
Land uses in coastal areas include tourism, industry, fishing, trade and transport. •
Censes data
A census is the procedure of systematically acquiring and recording information about the members of a given population.
Tale 2.2 describes the social parameters, physical parameters and the data source for making an assessment of coastal variability. Data Population
Data Summery Census Data
density Land use Map
Economics
and
Statistics
department LANDSAT Image
SOCIAL PARAMETERS
Source
USGS Site (http://earthexplore.usgs.gov)
Road network
Road
Google Earth
Slope
SRTM DEM
USGS Site (http://earthexplore.usgs.gov)
Elevation
SRTM DEM 43
USGS Site
(http://earthexplore.usgs.gov) Sea Level Rise
Tide Gauge Data
Global Sea Level Observing
PHYSICAL PARAMETERS
System(GLOSS) Shoreline
Satellite Images, Digital USGS Site
Change Rate
Shoreline analysis system (http://earthexplore.usgs.gov)
Tidal Range
Tide Tables
Harbor
Engineering
Department, Govt. of Kerala
Table 2.2 Data Parameters
3.2. Methodology using Fuzzy logic toolbox in mat lab Fuzzy logic toolbox provides graphical user interfaces mat lab function and Simulink blocks for designing and simulating fuzzy logic system.
3.3 . Fuzzy logic model A fuzzy logic model is a logical-mathematical procedure based on an “IF-THEN” rule system that allows for the reproduction of the human way of thinking in computational form. Vulnerability models are presented by fuzzy logic approaches by using input and output variables (Fig. 3.1)..
Flow Chart of Project Activities Kerala Coastal area
Coastal vulnerability
Nature of coast
Vulnerability and its impact study by using different data
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Impact of coastal vulnerability
Generate Fuzzy logic Model with fuzzy decision making technique
Analysis and recommend stable preventive measures Communicate the result with adequate supporting to appropriate authority for corrective action Figure 3.1 Block diagram: Flow Chart of Project Activities
3.4 Kerala Coast One of the coastal problems to be considered in Kerala is mainly coastal erosion. About 80% of the entire coastline of Kerala is affected by long term coastal erosion and part of the coastline of Kerala is affected by erosion and accretion in monsoon months. Natural factors that cause erosion in Kerala are heavy rainfall, loose sandy sea shore, destruction of mud deposits in the sea, and heavy discharge of water devoid of alluvium. The assessment of coastal vulnerability is done mostly on the basis of vulnerability indices. One of the most initial attempts to formulize a Coastal Vulnerability Index (CVI) particularly for sea-level rise was developed by Gornitz and Kanciruk (1989) for the United States. Fuzzy and probabilistic modelling, Cowell et al. (2006) presented a GIS-based model that integrates an existing predictive model using a differential approach, random simulation, and fuzzy set theory for predicting geomorphic hazards subject to uncertainty in New South Wales for beach vulnerability. There were attempts to calculate CVI of various coasts by taking only physical parameters. However, a major inadequacy in most of the vulnerability assessments is that they focus only on the physical characteristics of vulnerability. In majority of these studies, the CVI is expressed as the square root of the product of the ranking factors divided by the number of parameters considered. Analytic Hierarchy Process (AHP) based CVI assessment considering both Physical Vulnerability Index (PVI) and Social Vulnerability Index (SVI) had also been developed. 45
In the coastal zones, the Delta is subjected to the impact of climate change (i.e. sea level rise). Traditionally, the adaptation measures that were identified to deal with the impact of climate change on coastal zone areas include beach nourishment, construction of groins and breakwaters, tightening legal regulations, integrated coastal zone management and introducing changes in land use. These traditional measures have become obsolete, and hence a multi-criterion analysis in ranking the effectiveness of the adaptation strategy is proposed and implemented for the strategy of this fuzzy decision making.
3.5 Coastal Vulnerability Index The computation of coastal vulnerability index involves the estimation of physical vulnerability index and social vulnerability index. The weights for PVI and SVI were then calculated using the AHP which is discussed subsequently. CVI was further computed using the calculated indices to understand the relative vulnerability of the study area. The present study considered nine variables that can be classified into two groups: 1) physical variables and 2) social variables. The physical variables include tidal range, significant wave height, sea level rise, shoreline change rate, slope and elevation. The social variables include population density, land use and road network. These parameters were derived from GIS analysis, remote sensing data and field data pertaining to the study area under consideration. The schematic diagram showing the methodology used in the study is shown in Fig.4.1 under chapter IV., with the coastal vulnerability subdivided into two components namely physical vulnerability and social vulnerability along with the associated variables used for their computation.
3.6 Data Ranking The values of variable were assigned a vulnerability ranking based on value ranges from vulnerability point of view. Each variable was ranked from 1 to 4 representing very low, low, high and very high respectively. In other words, a value of 1 represents the lowest risk and 4 represent the highest risk. The database includes both quantitative and qualitative information. Thus, numerical variables are assigned a risk ranking based on data value
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ranges, while the non-numerical land use variable is ranked according to their relative resistance of a given landform to vulnerability.
A multi-criterion analysis, in ranking the effectiveness of the adaptation strategy, was proposed and implemented. This strategy is the fuzzy decision making. It was found that such a model is suitable in evaluating situations that deal with imprecise information. Using fuzzy decision making technique enables maximum benefit as it takes into account several variables. Primarily, the literature in the field of climate change and decision making was revised. A study area was chosen to be investigated. The decision-making approach was studied. The set of alternatives was steadfast and evaluated. A comparison between the alternatives was established.
The investigation phases of the project •
Reviewing the literature in the fields of climate change adaptation techniques and sea level rise.
•
Choosing a study area ( Kerala coastal region)
•
Investigating the impact of sea level rise on the study area.
•
Examining the vulnerability and adaptation of the study area.
•
Investigating the vulnerability index in the study area.
•
Examining the vulnerability indicators in the study area.
•
Investigating the available techniques and choosing the Fuzzy Decision Making Technique (FDMT) to be implemented in the selected coastal region.
•
Studying the FDMT and applying it to the study area.
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CHAPTER –IV RESULT
4.1 Fuzzy vulnerability assessment model result A. Fuzzy Logic Approach and decision making: Fuzzy logic provides an alternative way to represent linguistic and subjective attributes of the real world in computing. Every decision with Boolean logic either 0 or 1. Fuzzy logic allows for membership functions, or degrees of truthfulness and falsehood that resulting the fuzzy model. Fuzzy Decision Making employs fuzzy technologies to obtain optimal solutions and support in making the best possible decisions and it provides advancing practice in the presence of uncertainty. The contribution of this approach for this study is to foster an understanding, development, and practice of fuzzy technologies for describing and making an assessment for the vulnerability of Kerala coast for factors like sea level rise, tidal range and coastal erosion. B. Use of analytic hierarchy process (AHP) for fuzzy vulnerability assessment model: Coastal Vulnerability Index (CVI): The computation of coastal vulnerability index involves the estimation of physical vulnerability index (PVI) and social vulnerability index (SVI). The weights for PVI and SVI are then calculated using the AHP. CVI was further computed using the calculated indices to understand the relative vulnerability of the study area. The study by Greeshma and Jairaj (2014) considered nine variables that was classified into two groups: 1) physical variables and 2) social variables. The physical variables include tidal range, significant wave height, sea level rise, shoreline change rate, slope and elevation. The social variables include population density, land use and road network. These parameters were derived from GIS analysis, remote sensing data. Data Collection Social Parameter
Data Processing
Physical Parameter
Data Ranking Weight assignment using AHP
Coastal Vulnerability Index
Figure 4.1 Block diagram: flowchart of CVI calculation 48
4.2 Methodology The computation of coastal vulnerability index involves the estimation of physical vulnerability index (PVI) and social vulnerability index (SVI). Weight assigning using analytic hierarchy process (AHP) are discussed. Then CVI is computed using the calculated indices.
I. Weight assigning using analytic hierarchy process (AHP) To prioritize coastal areas according to their vulnerability to sea level rise is one of the objectives of the application of the fuzzy vulnerability assessment model which requires ranking of coastal areas. The consistency property of matrices have to be checked to ensure that the weights given were consistent. For this, Consistency Index (CI) and Consistency Ratio (CR) are selected as given by Saaty (1977). CONSISTANCY INDEX (CI) is represented as CI = (λmax−n)/ (n−1)
(1)
CONSISTANCY RATIO (RI) CR = CI/RI
(2)
Where λmax is largest or principal value of the matrix, n is the order of the matrix and RI is the Random consistency Index. Random consistency index table developed by Saaty (1994) as follows; RI is defined as the average of the resulting consistency index that depends on the order of the matrix given . The values for RI for different order of the matrix (N) are given in Table 4. 1.
N RI
2 0
3 0.52
4 0.59
5 1.12
6 1.24
Table 4.1 RI values for different order of the matrix
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7 1.32
8 1.41
II. Coastal vulnerability assessment The weights derived using AHP are used for calculating the Physical vulnerability index (PVI) and Social vulnerability index (SVI) using formula (3) and (4) PVI: Population density, land use map, road network SVI: Slope, elevation, sea level rise, shoreline change, tidal range etc. SVI = w1X1 +w2X2+w3X3
(3)
PVI = w1X1+w2X2+ w3X3+w4X4+w5X5+w6X6
(4)
Where w1 is the weight value of ith (i=1, 2….) social or physical variables, and X1 is the vulnerability score of ith variable. Coastal Vulnerability Index was calculated as the average of SVI and PVI using CVI = (SVI+PVI)/2 The above equation
(5) had been used considering that both physical and social variables
have equal contribution in coastal vulnerability. The physical parameters taken as secondary data of Coastal vulnerability assessment along Kerala coast using Remote Sensing and GIS taken for the study by Greeshma and Jairaj (2014). III. Data Ranking The values of variable were assigned a vulnerability ranking based on value ranges from vulnerability point of view. Each variables was ranked from low, high and very high respectively and put it in the Fuzzy logic tool box in MATLAB Fuzzy logic tool box in Mat lab platform -Key Features • •
Fuzzy Logic Design app for building fuzzy inference systems and viewing and analyzing results Membership functions for creating fuzzy inference systems 50
•
Support for AND, OR, and NOT logic in user-defined rules
•
Standard Mamdani and Sugeno-type fuzzy inference systems
•
Automated membership function shaping through neuro adaptive and fuzzy clustering learning techniques
•
Ability to embed a fuzzy inference system in a Simulink model
•
Ability to generate embeddable C code or stand-alone executable fuzzy inference engines
Balancing a pole on a moving cart. The system, which is similar to an inverted pendulum, uses a Fuzzy Controller block within Simulink to balance the pole.
IV. Working with the Fuzzy Logic Toolbox The Fuzzy Logic Toolbox provides applications to perform classical fuzzy system development and pattern recognition. Using the toolbox, we can do the following •
Develop and analyze fuzzy inference systems
•
Develop adaptive neuro fuzzy inference systems
•
Perform fuzzy clustering
In addition, the toolbox provides a fuzzy controller block that you can use in Simulink to model and simulate a fuzzy logic control system. From Simulink, you can generate C code for use in embedded applications that include fuzzy logic
1. Fuzzy Logic Designer By saving to the workspace with a new name, we can also rename the entire system.
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Figure 4.2 Define input and output.
Figure 4.3 Go to edit-Add variable input, here set as 3 input variable
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Figure 4.4 Name each input variable
2. Membership Function Editor Define membership function
Figure 4.5 Go to edit and delete existing membership function and add new membership function 53
Figure 4.6 Define membership function for input-1, add values& ranges
Figure 4.7 Define membership function for input-2, add values& ranges
54
Figure 4.8 Define membership function for input-3, add values& ranges
Figure 4.9 Define membership function for output, add values& ranges
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3. Rule Editor
Figure 4.10 Define rule- close membership function and go to edit the rule 4. Rule Viewer
Figure 4.11 Go to view menu and select rules will get rule viewer interface 56
Figure 4.12 Rule viewer interface 5. Surface Viewer
Figure 4.13 Simulation in surface viewer 1 57
Figure 4.14 Simulation in surface viewer 2
Figure 4.15 Simulation in surface viewer 3
58
Figure 4.16 Simulation in surface viewer 4
Figure 4.17 Simulation in surface viewer 5
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Figure 4.18 Simulation in surface viewer 6
Figure 4.19 Simulation in surface viewer 7
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Physical parameters such as Sea level rise, Shoreline change, Tidal range for different Kerala coastal regions (Table 4.2 and 4.3) are applied into fuzzy logic system. Ranking rules are already set in fuzzy system by using the different conditions of input parameters and developed and plotted (Fig.4.2 -4.19). When changing the input values of each coastal region gives the appropriate vulnerability ranking. Thus fuzzy system generate fuzzy vulnerable model for each coastal region . COASTAL VULNERABILITY RANKING BASED ON FUZZY MODEL LOW TO HIGH -Rank 0 To Rank 5 Rank 0 Rank 1 Rank 2 Rank 3 Rank4 Rank 5 Thrissur Payyambalam Kollam Azheekal Nil Nil Snehatheeram Ponnani Bekal beach Poovar Arattupuzha Kozhikodu Fortkochi Cherthala Cherayi Table 4.2 Coastal vulnerability ranking based on the fuzzy model output
COASTAL VULNERABILITY RANKING BASED ON FUZZY MODEL Low Vulnerable Thrissur Snehatheeram Ponnani Bekal beach Payyambalam
Medium Vulnerable Kollam
High Vulnerable Azheekal
Kozhikodu Poovar
Arattupuzha Fortkochi
Cherthala
Cherayi
Very High Vulnerable Nil
Table 4.3 Vulnerability assessment of selected beaches along the Kerala coast using fuzzy model
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CHAPTER –V DISCUSSION Coastal areas are one of the major systems for global sustainability. These are the transition areas between land and sea. Coastal regions gained importance because of multiple uses like highly concentrated population, high productivity of the ecosystem, industrial friendly, tourism, transportation etc. Coasts are always in a dynamic state trying to change. India, with most diverse ecosystem, high productivity and thickly populated over coastal region. Despite all of these, Indian coasts are under threat due to multiple parameters like physical and human parameters. These stresses are driving vulnerabilities like sea-level rise, coastal erosion, flooding etc. In this critical scenario, coastal management has become one of the very important issues in the last years. Thus, coastal vulnerability assessment model developed to identify and manage the vulnerable areas over the coast. This study focuses on the different vulnerabilities at the coast of Kerala and generate human logics from coastal vulnerability index methodology, then applied this logic to fuzzy logic system. Fuzzy Vulnerability assessment model developed by fuzzy logic toolbox on MATLAB. The process where we identify the vulnerability of coastal regions. Assess the risk rate and reduce vulnerabilities by proper planning and adaptation strategies. The purpose of fuzzy rule bases is to formalize and implement a human being’s method of reasoning. The tool most commonly used in fuzzy logic applications is the fuzzy rule base.. A rule is of the type: IF “predicate” THEN “conclusion”. For example: IF “high temperature and high pressure” THEN “strong ventilation and wide open valve”. We need to capture the essentials of this problem, leaving aside all the factors that could be arbitrary.
V. Building a Fuzzy Inference System Fuzzy inference is a method that interprets the values in the input vector and, based on user-defined rules, assigns values to the output vector. Using the editors and viewers in the Fuzzy Logic Toolbox, you can build the rules set, define the membership functions, and analyze the behavior of a fuzzy inference system (FIS). The following editors and viewers are provided: 62
DEFINE INPUTS
CREATE MEMBERSHIP FUNCTION DEFINE THE RULES OF FUZZY LOGIC
RULE EDITOR
RULE VIEWER
SURFACE VIEWER
FIGURE 5.1. Over all procedure for fuzzy logic system
A. FIS Editor - Displays general information about a fuzzy inference system •
Three inputs and one output is defined in figure 4.4.The input variables are sea level rise, shoreline change, tidal range and output set as rank of vulnerability.
Membership Function Editor - Lets you display and edit the membership functions associated with the input and output variables of the FIS.A fuzzy set is defined by its “membership function” which corresponds to the notion of a “characteristic function” in classical logic. The membership function of a fuzzy set is a generalization of the indicator function in classical sets. A membership function (MF) is a curve that defines how each point in the input space is mapped to a membership value.The input space is sometimes referred to as the universe of discourse, a fancy name for a simple concept. Vulnerability membership functions are defined by measurable parameters.
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•
Membership functions and ranges of values declared in figure 4.6 to figure 4.9
B. Rule Editor - Lets you view and edit fuzzy rules using one of three formats: full English-like syntax, concise symbolic notation, or an indexed notation. IF-THEN rule used for vulnerability assessment study. The below parameters are considered for decision making using fuzzy logic. The logic is built-in operator of MATLAB Fuzzy Logic Toolbox. • • • • • • •
If sea level rise is low and shoreline change is low and tidal range is low then rank of vulnerability is rank0 If sea level rise is low and shoreline change is low but tidal range is high then rank of vulnerability is rank1 If sea level rise is high and shoreline change is high but tidal range is low then rank of vulnerability is rank2 If sea level rise is high and shoreline change is high and tidal range is high then rank of vulnerability is rank3 If sea level rise is very high and shoreline change is very high but tidal range is low then rank of vulnerability is rank4 If sea level rise is very high and shoreline change is very high and tidal range is high then rank of vulnerability is rank5 Rules defined in figure 4.10
C. Rule Viewer - Lets you view detailed behavior of a FIS to help diagnose the behavior of specific rules or study the effect of changing input variables •
Figure 4.11 and figure 4.12 shows the rule viewer interface
D. Surface Viewer - Generates a 3-D surface from two input variables and the output of an FIS •
Figure 4.13 to figure 4.19 shows the surface viewer
A. Adaptive Neuro fuzzy Inference Using the Neuro-Fuzzy Design app, you can shape membership functions by training them with input/output data rather than specifying them manually. The toolbox uses a back propagation algorithm alone or in combination with a least squares method, enabling your fuzzy systems to learn from the data.. 64
VI. Simulating and Deploying Fuzzy Inference Systems (FIS) We can evaluate FIS performance by using the Fuzzy Logic Controller block in a Simulink model of your system. The Fuzzy Logic Controller block automatically generates a hierarchical block diagram representation for most fuzzy inference systems. This representation uses only built-in Simulink blocks, enabling efficient code generation (using Simulink Coder, available separately). A. Fuzzy Logic Controller in Simulink The toolbox supplies a fuzzy inference engine that can execute fuzzy system as a standalone application or embedded in an external application. The overall coastal vulnerability index which defines the aggregated vulnerability enables decision makers to compare different regions. And then developed the fuzzy vulnerability assessment model in MATLAB fuzzy logic toolbox. The fuzzy vulnerability assessment model enables the user to assign different weights to each parameter and this would act as a general framework for coastal zone management practices focusing on adaptation measures. B. Result and Mat Lab Execution Parameters
Coastal Vulnerability Ranking
Very Low Low High Very high Tidal Range(m) 0- 0.25 0.25-0.5 0.5-1 >1 Sea level rise(mm) 0-20 20-40 40-60 >60 Slope(degrees) >18 12-18 6-12 0-6 Table 5.1.0 Vulnerability ranking criteria for physical variables Parameters Tidal Range Sea Level Rise Slope
Weight values 0.03 0.08 0.46 Table 5.2.0 Weights obtained from AHP process
Combine parameters index values of the respective Kerala coastal regions. And then CVI value of the regions derived. The coastal stretches of Kerala classified as Very Low, Low, High and Very High vulnerability based on the vulnerability values. Analyzing these range of values and apply this range of values as Low, High and Very High in to fuzzy logic system. Fuzzy logic toolbox provides graphical user interface for design and stimulation.
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Table 4.2 and Table 4.3 shows Kerala vulnerability ranking by analyzing the fuzzy model output
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CHAPTER VI SUMMARY Fuzzy vulnerability model is successfully applied to some of the coastal regions of Kerala having different physical and social subsystem characteristics. Three different cases act as validation of the fuzzy vulnerability model through comparison of the results with preliminary model results (which were validated through available literature) and ensured that the model is also applicable at local levels by highlighting the small differences within a small spatial scale. coastal areas (both at physical and human activity levels) to ensure the applicability of the model in large spatial scales such as regional to national levels - The Fuzzy Vulnerability Assessment Model uses fuzzy logic theory as the uncertainty model underlying the vulnerability assessment model to overcome the limitations of crisp boundaries of classification of data, non-linear relationships or unknown relationships between the parameters of social and physical systems and description of linguistic variable (vulnerability). A graphical user interface was developed on MATLAB platform to ensure ease of applicability of the end users. Two graphs can be generated; impact graph showing the impact vulnerability indices and influence histogram showing the influence of natural and human subsystems over individual impacts. The automation of the model structure increases the efficiency of the vulnerability assessment model as well as applicability for real case studies. The outcome of the Analytic Hierarch Process (AHP) analysis shows that integration of anthropogenic activities and physical processes needs to be considered when implementation of any coastal region. This is established into fuzzy vulnerability assessment model for the coastal areas of Kerala. The use of fuzzy set theory enabled to include uncertainty modeling of the vulnerability assessment problem through which real system can be represented more accurately. The uncertainty profile of the fuzzy vulnerability assessment model (FCVI) and the uncertainty vector fuzzy set theory was compared to validate the use of fuzzy set theory. The sensitivity of the model parameters is low and the model is robust, uncertainty is mostly defined by model structure in terms of spread of the outcome of fuzzy set. Comparison of different shapes of membership functions validated the use of triangular membership functions since they are easier to generate and the uncertainties are low. Influence of implication and aggregation operators of 67
the fuzzy inference system were analyzed. The uncertainty analysis showed that the chosen operators ensured that the most critical case for vulnerability was assessed. The overall aim of vulnerability assessment studies is to bridge the gap between current knowledge of adaptation measures, adaptation and mitigation to a specific problem. This study through the development of fuzzy vulnerability assessment model achieves this objective successfully. The output values of the model enables decision makers to interpret the characteristics of the coastal area, understand the present status of the region, predict the possible vulnerability in the future (if the same trend continues), also the possible ranges of vulnerability in the future (if different trends prevails) and suggest possible adaptation options in general through the influence histogram. The uncertainty being a part of the whole modeling procedure is a recent concept proposed by several researchers, however with regards to coastal vulnerability assessments; this type of assessment was first applied within this study. Both the model outcomes and the integration of uncertainty strengthen the decision making process and can very well change the perception of stakeholders on implementation of measures for future sea level rise by giving them possible frameworks to base their decisions on. A decision model for evaluating the intensity of resistance (reliability) strategies based on fuzzy logic theory has been proposed. The model is exemplified by simple criteria comprising: sea level rise and adaptation. Modification of the model to cover other criteria such as desertification, water resources availability and cost can be easily incorporated. The model can be used by decision-makers to choose the most appropriate reliability strategy. The model emphasizes the multi criterion aspect of climate change evaluation, the qualitative, subjective, and non-interactive nature of these criteria, and their aggregation in the evaluation process. It should be emphasized that non-interactivity is one of the important features that underline the fuzzy aggregation procedure. Non-interactivity simply means the ratings assigned to major criteria such as sea level rise and adaptation which does not compensate each other. It has been noted that the proposed procedure using fuzzy sets is just an addition to the range of multi criterion decision methodologies. The particular characteristic of the present procedure is its emphasis on the human perception nature, the qualitative or subjective criteria used in reliability, and the theoretical basis for aggregation of these criteria.
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6.1 FUTURE OUTLOOK AND RECOMMENDATIONS Coastal vulnerability has been increased for the last many years due to different types of natural and human induced changes. Most scientific analysis and assessment of such risks focus on the environmental changes and the aim of carrying out a vulnerability assessment study is to help to achieve a sustainable use of coastal resources by identifying coastal vulnerability and incorporating coastal values and hazards in to planning and decision making for integrated coastal zone management. This study
introduces Fuzzy logic decision-making framework that are useful for
considering choices about climate change responses through the complementary strategies of adaptation and mitigation.. Fuzzy logic is an artificial intelligence that helps to deal with the uncertainty in engineering, and this opens the possibilities of the application in coastal zone to maintain the sustainability. Different types of models are already proposed by experts. Human decisions can control the fuzzy system by infinite number of input variables and by creative rules. This study took only three major variables as inputs (sea level rise, shoreline change, tidal range) and assigned output as rank of vulnerability. The advantage of definite input of Fuzzy logic system will allow us to consider many other parameters as input for further study. Also there is possibility to create a website based on ASP.NET by using C Sharp as front end and SQL database as back end, so that common people/authority can access the vulnerability information from everywhere using the real time data. As per the saved ranking data. the system can detect the rank of vulnerable areas (which need immediate adaptation/prevention procedures) and then immediately inform an alarm message to appropriate authority. In order to meet the challenges of coastal vulnerability, coastal protective measures is an option. Incorporation of ecology and ecosystem services into coastal protection has two main reason: First, there is a strong need for innovative, sustainable and cost effective coastal protection solutions that deal with threats related to climate change, such as accelerating sea level rise and Second, there is need for measures that minimize anthropogenic impacts of coastal protection structures on ecosystems and that might perhaps even offer possibilities to enhance ecosystem functioning. The following is a catalogue of relevant techniques that could be employed as coastal management techniques, such coastal protection measures are mainly 69
focused to protect the coastal areas. It is further classified into soft engineering and hard engineering method. Erosion processes remove land from some parts of the coastline, whereas deposition processes create new land in other places. In addition, the fact that the sea level is rising locally and globally could add to these erosion and deposition problems whilst also removing land from use at the coastline. It is for these reasons that human beings have long sought to control and manage the coastline. However, there is a huge debate as to how to do this - either by using hard engineering or soft engineering.
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