earthquake vulnerability assessment of urban areas ...

EARTHQUAKE VULNERABILITY ASSESSMENT OF URBAN AREAS IN INDIA, AN IT BASED APPROACH Ranjeet Joshi Graduate Student, Computer Aided Structural Engineering IIIT Hyderabad India E-mail: [email protected] and Ramancharla Pradeep Kumar Earthquake Engineering Research Centre IIIT Hyderabad India E-mail: [email protected] ABSTRACT India shares over 16% of world population with only 3.4% of landmass. This has resulted in the dispersion of population to disaster prone regions. Therefore even moderate earthquakes are resulting in high damage estimates. Growing opportunities, better health care and good education is holding a significant population in Indian mega cities but almost all of these cities lie in Seismic zone III or above which is having the possibility of earthquakes up to intensity VII (MSK) or more. All these factors necessitate an effective Disaster Management System in India. This paper proposes an IT enabled approach towards locating Immediate Focus Region (IFRs). IFRs are regions that require immediate attention in areas that may vary from population densities to number of hospitals in a particular region. Although data required to mine out IFRs is available, it is scattered across different departments of the Government of India. Hence an appropriate model for a centralized database of all relevant data is of paramount priority in aiding decision making before, during and after a particular disaster. Prediction of an earthquake is still an ongoing research. The most intelligent approach in present scenario is to focus on the damage assessment as a function of property and life. The purpose of this study is to integrate different census data (pertaining to property damage risk and population) for the development of an effective disaster management system. 1. INTRODUCTION Earthquakes and other natural hazards cause innumerable deaths, injuries, and billions of dollars in economic losses each year around the world. Every passing year is simply increasing the accrued amount spent on the recovery of losses caused by various natural disasters. Moreover the

November 2006, Thailand

focus for fighting against these calamities is not well defined. Major care is taken of the regions affected by any such disasters; no primary work is going on in the field of identification and safety of vulnerable areas. With its vast terrain, large population and distinct climatic conditions, India is prone to a number of natural hazards. Among all the natural disasters, earthquakes are the most frequent and the most vulnerable disasters, causing millions of people loosing their lives and properties every year. The country has faced some severe earthquakes causing widespread damage to the life and property. India has a coastline of about 8000 km which is prone to Tsunami at point of time in future. Another major problem faced by the country is in the form of population. Overall the vulnerability of disasters in the country has risen in past few years. Vulnerability information tool (VIT) was developed with a vision to centralize the data relevant to various factors affecting the disaster management. As its name suggests the primary focus of this application is to provide the vulnerability information of a particular region indexed with few other attributes such as the count of houses, population, emergency action units etc. Hence this application is developed as a decision support system needed before, at the time and after any natural calamity. 2 Vulnerability Assessment Urban India today contributes more than 60% of the Gross Domestic Product (GDP). Moreover four Indian metro cities hold a position in the list of World’s twenty largest urban agglomerates. Again the most shocking fact is a fact of these regions/cities being among the most vulnerable places to natural disasters. Apparently, these facts support a quick action to be taken in the area of disaster preparedness of these areas. Twenty largest urban agglomerates ranked by the population size in 2000 are enlisted in Table 1. 2.1 Developing Economy Facts Following are the facts of developing economy. 1) Approximately 13 times more people die per reported disaster in developing countries than in developed countries. 2) From 1992 to 2001, developing countries accounted for 20% of the total number of disasters, and over 50% of all disaster fatalities. 3) Developing countries suffer the greatest costs when a disaster hits. More than 95% of all deaths caused by disasters occur in developing countries; and losses due to natural disasters are 20 times greater (as a percentage of GDP) in developing countries than in industrialized countries. Poorly planned development can turn a recurring natural phenomenon into a human and economic disaster. Allowing dense populations on a flood plain or permitting poor or un-enforced building codes in earthquake zones is as likely as a natural event to cause casualties and losses. Similarly, allowing the degradation of natural resources further increases the risk of a disaster.

New Technologies for Urban Safety of Mega Cities in Asia

3. VIT DATA SYSTEM The overall system of the required data is so complex that its simplification for further enhancement in its usability is of prime concern. Based on these conceptual ideas the data is modularized, where each module represents a particular set of information inevitable for the effectiveness of any such system. The only problem during this data collection was the availability of data–sets through different sources. Data is available but is scattered across different departments of Government of India. Following are the main data modules and respective sources from where the data was collected: 3.1 Data Modules & Respective Sources 3.1.1 Data Modules The data module structure is classified into three major categories, property data, population data and risk data respectively. Moreover it was structured in such a way that its extension was kept open for further modifications. Further the data - sets responsible for the study of actual and possible losses of properties and people in disaster prone areas are taken as 1) Number of houses (collection based on types of roofs and wall indexed to a district level) 2)Total population (total male/female population, total workers, total employees) 3) Soil type indexed to a district level 4) Heritage structure: Name, numbers and number of visitors of various important heritage structures indexed to a district level. Again table structure was kept flexible for any further extension. Estimated Risk pertaining to disasters such as Earthquakes, floods and winds based on various categories of houses again indexed to a district level. 3.1.2 Data Sources The data was collected from the following resources: Vulnerability atlas of India: This is an atlas prepared by Building materials and technology promotional council (BMTPC) so as to project a combination of local hazard intensity and vulnerability of existing house types for carrying out risk analysis given in the district-wise tables. All these datasets were available for generic categories of urban and rural areas. Census of India, 2001: A manual encapsulating the population data along with categories based on gender, working, non working, employees, nonemployees based on the survey undergone in the year 2001. All these datasets were available for generic categories of urban and rural areas.

Earthquake Vulnerability Assessment of Urban Areas in India: An IT Based Approach


3.2 Entity- Relationship (ER) Model After collecting datasets from various sources the successive work was to incorporate them into an effective model without loosing the extensibility as a prior concern. Based on this focus an ER model was prepared, as shown in figure 1, with all the functionalities of existing data along with the provision for further extension. 3.3 General flow diagram VIT development was a two way process. The first one was the development of back- end and the other was the development of front- end for this application. Later on the two ends were integrated together to finalize the VIT application. Back – end was related to the building, linkage and access of data from the database; while front end was focused on the retrieval and projection of data to the end users. Back- end operations were responsible for the overall accuracy of the application development and the front end for all the aesthetic views for better user – friendliness of the application. Together back- end and the front – end can be diagrammatically represented as shown in figure 2. The application framework was divided into two fragments, the front – end and the back – end. The query processor interacts with the database and retrieves information queried through the direct, aggregate and interlinking queries. Finally the retrieved information through distinct queries is sent to the front- end so as to be viewed by the end user. This idea of creating centralized database extends our power to process interlinked queries related to data- set collected from different sources. 3.3.1 Introduction to back end As mentioned earlier, back – end was related to the building, linkage and access of data from the database; while front end was focused on the retrieval and projection of data to the end users. The application was developed keeping in view the making of all these processes much easier to operate. The only problem with such a database is the susceptibility of these datasets to further modifications. With time specified or unspecified all these data sets can be modified. The actual database can be accessed and altered through a command prompt as shown in figure 3. Again these modification techniques are more supportive to database experts. Since this application was very much tend to modifications, a user friendly interface was developed as shown in figure 4. Moreover to avoid redundant repetition of the same data, validations were also included in the class hierarchy. Class Building < ActiveRecord::Base validates_presence_of :DISTT_ID, :CAT_ID, :bid, :ROOF_TYPE, :AREA_ TYPE, :NUM_HOUSES validates_numericality_of:NUM_HOUSES validates_uniqueness_of:DISTT_ID, :bid end


2.3.1.1. Development Environment Database was created in MYSQL and the web application was developed in Ruby on Rails. MYSQL: MySQL is a multithreaded, multi-user, SQL database management system (DBMS) with more than six million installations. MySQL is available as free software under the GNU General Public License (GPL). Ruby on Rails (RoR): Ruby on Rails is also known as RoR, or just Rails, is an open source web application framework written in Ruby that closely follows the Model-View-Controller (MVC) architecture. It strives for simplicity and allowing real-world applications to be developed in less code than other frameworks and with a minimum of configuration. The Ruby programming language allows for extensive metaprogramming. 3.3.2 Introduction to front end The front- end is developed as a window’s application in Microsoft Visual Basic 6.0. The major focus during development was to retain the aesthetics with the user- friendliness of the application as shown in figure 5.

3.3.3 Immediate Focus Regions (IFR’s): IFR’s are the regions identified as a result of cumulative queries generated over the encapsulated data. The reason behind classifying regions as IFR’s is challenging the possibilities of various risk factor combinations on the basis of their cardinality for any devastation. For e.g. a region with very high risk of an earthquake and with a medium or very low population is indexed low in terms of an IFR as compared to a region with a medium risk to an earthquake and a very high population. This is a very simple example but if all the factors are analyzed and combined subtlety then very informative results, from the point of view of disaster management, can be concluded. As shown in figure 6, identification of IFR’s becomes so lucid using an application like VIT. Every data corresponding to risk promoting factors such as population and earthquake risk are projected and analyzed together on the same interface. This is one of such combinations. Many more such combinations are still behind the scenes that are needed to be recovered. 4. CONCLUSIONS India is not in the list of top 10 countries most exposed to multiple hazards, but it holds the top position in the list of top 10 countries with the most World Bank- funded disaster projects and the top 10 largest loans for disasters.” There is a history of devastating disasters and their influences on us are quantifying day by day.



Above points suggests a growing trend of international loans requirement to cope up with disasters hitting India every year. India is in great need of vulnerability assessment studies so as to reduce the losses caused by various natural hazards. This is possible only as a part of an effective disaster management system. If some actions are not taken in the direction of these studies, India will sooner become economically handicapped in the international market by these disasters even if will be growing good in other sectors of development.

REFERENCES Vulnerability atlas of India: building material and promotional council Census data of India- 2001, Survey of India. Anil K. Chopra: Dynamics of structures, theory and application to Earthquake engineering, 2nd edition, prentice hall publications. Elmasri – Navathe, Fundamentals of database systems, Addison – Wesley Longman publishing co, Inc. Dave Thomas, David Heinemeier Hansson, Andreas Schwarz, Thomas Fuchs, Leon Breedt, Mike Clark, Agile web development with Rails: a pragmatic guide, 2nd edition, pragmatic bookshelf.

Figure 1: Entity Relationship (ER) Model


Table 1 Twenty largest urban agglomerates ranked by the population size in 2000 Sl No

Urban Area

Country

Population (million)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Tokyo Greater Mumbai Mexico City Sao Paulo New York Shanghai Lagos Los Angeles Kolkata Dhaka Buenos Aires Karachi Delhi Beijing Jakarta Osaka Metro Manila Rio de Janeiro Cairo Tianjin

Japan India (2001 Census) Mexico Brazil USA China Nigeria USA India (2001 Census) Bangladesh Argentina Pakistan India (2001 Census) China Indonesia Japan Philippines Brazil Egypt China

26.4 18.7 18.1 17.8 16.6 13.8 13.4 13.1 13.1 12.7 12.6 12.2 12.1 11.8 11 11 10.9 10.6 10.6 10.1



Figure 2: General flow diagram

Figure 3: Data tables


Figure 4: Back – end user interface

Figure 5: front end user interface



Figure 6: Vulnerability assessment of Gujarat
