Flood Prediction Techniques Based on Geographical ...

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Flood Prediction Techniques Based on Geographical Information System using Wireless Sensor Networks Naveed Ahmed1, Atta-ur-Rahman2*, Sujata Dash3, Maqsood Mahmud3 1

Faculty of Engineering and Computer science, National University of Modern Languages (NUML), Islamabad, Pakistan 2* Deparment of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University (IAU), Dammam, KSA 3 Department of Computer Science & Application, North Orissa University, Odisha, India. 4 Deparment of Management Information System, College of Business Administration, Imam Abdulrahman Bin Faisal University (IAU), Dammam, KSA Email:

[email protected], *[email protected], [email protected], [email protected]

Abstract: This paper presents a comprehensive study of flood forcasting, analysis and prediction using geographical information system (GIS) and wireless ad hoc sensor networks. The role of science and technology has moved the research towards new horizon, where scientists and engineers from all over the world are using GIS based techniques for flood prediction and hydrological risk analysis. The radar satellite images are also most frequently used for identifying the flood catchment areas in specific disaster zone. The GIS domain also proves to be very helpful for us in geographical survey and to identify the tsunamis causing vast potential and economical damage. The input parameters for flood forecasting are also used for modeling GIS, depending on the environmental conditions and climatic parameters such as soil moisture, air pressure, direction of wind, humidity and rain fall . The core objective of this research is to study various GIS based flood forecasting techniques. In this research study we have proposed a GIS based flood forecasting model using neural network based approach. Our proposed model is helpfull for the researchers in predicting the upcoming disasters and to take necessary actions by the rescue authorities to save the life of thousands of people to be suffered from this critical circumstance. Keywords: GIS, Flood Forcasting Techniques, Wireless Sensor Networks, Particle Swarm Optimization, Artificial Neural Fuzzy Inference Systems

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Introduction

Flood prediction and river flow modeling is one of the important problems that have attracted the large number of scientists from all over the world. Now a days availability of accurate flood forcasting technique and methods helps in the reduction of Floods and droughts. Ad hoc wireless Sensor Networks are significantly used in Real Life applications especially the major application areas includes Telemedicines, wireless body area networks vehicle ad hoc networks, underwater Wireless sensor networks and Disaster Management. The domain of the Disaster Management includes River Floods, hurricanes,fires and Earth quakes which provides a great danger and risk factor for the existing human population. Wireless Sensor Networks can be classified as low power multi hopping system capable to transfer the information from one node to another via using Ad hoc Relay Stations. Different researchers and scientistis had proposed the diversified architecture for Disaster survivor detection in critical circumstances using Adhoc wireless sensor Network Architecture. Our major objective in this research is to design a comprehensive architecture for flood risk assessment using Geographical Information system based approach along with its integration with ad hoc wireless sensor network. The core issue for this research mainly depends upon classification of determining flood level duration and its intensity before this extreme event occurs. The data sets collected from the sensors nodes are used for flood forecasting using neural network based approach [1]. The complex problem of flood forecasting can easily be solved efficiently using GIS, WSN and Artifical Neural networks based technique. The Research work also focuses on the design of hydrographs that indicates the location of possible discharges. Remainder of paper is structured as follow. Section 2 discusses Flood Prediction techniques, Section 3 presents Proposed Model for Flood forcasting using ANNFIS, Section 4 presents proposed methodology based on input parameters for flood forcasting and finally conclusion and future research direction are presented in Section 5.

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Flood Prediction Techniques

This section provides the review of existing Flood forcasting techniques in the literaure. 2.1 Existing Flood forcasting techniques and models Rao et al. [2] had developed the flood forecasting Model for Godavari Basin. The Author had also proposed the distributed Modeling approach for topographic and metrological parameters which are used to calculate the extreme Runoff process. The Kinematic wave mathematical model is designed to achieve runoff Model which helps in predicting the terrains. The soil conservation service (SCS) Unit hydrograph technique has been adopted to derive graphs from gauge rainfall data and runoff process from large number of water sheds. According to Biondi et al [3] stochastic and distributed Modeling technique has been used for numerical weather prediction in case of river discharge. The mechanism for using ensemble flood forecasting in case of heavy rainfall arises, particularly to predict Flood warnings earlier before the critical condition occurs. The solution for Flood forecasting is determined in simplest form, based on probabilistic Flood prediction which is very useful factor in obtaining the estimation of Flood Risk [4]. Fiorentio et al [5] had performed the detailed analysis of flood frequency distribution and compared the simulation results with distributed hydrological modeling (DREAM) along with rainfall generator scheme (IRP). The Methodological scheme has been adopted which consists of occurrence of dry and rainy intervals on the basis of exponential (wet) and weibull (dry) distribution. According to Rozalis et al [6] Flood events are generated by extreme rainfall events with relatively high rain fall intensity due to thunder storms. One of the main objectives of the current research is to use relatively simple and flexible model that can be applied over gauged and un gauged water sheds. The Authors has also proposed the kinematic wave Method for determining Flood flow routing with in specified zone. The Model input parameters for rainfall is obtained directly from Radar

3 satellite images, along with the rain fall gauges. The Model performance was calculated by comparing different hydrographs over the study area of 20 selected locations for identifying peak Flow discharge, Runoff depth rain depth along with maximum rain rate. Ren et al [7] had presented a new classified real time flood forecasting frame work by coupling fuzzy clustering and neural networks with hydrological Modeling. The fuzzy clustering Model is used to classify the historical floods from the available flood records in different categories which is used to calculate flood peak and runoff depth.The conceptual hydrological model used for generating optimal set of parameters for flood prediction using genetic algorithm. The Artificial neural network Model is trained to predict the real time Flood events using the real time rainfall data. According to Nie et al [8] rapid climatic change is an important factor for the occurrence of Flash Flood events. The variation in the comprehensive Flood index in certain regions is due to functional decline of forests during the last few years. The changes in the Flood disaster is analyzed by using SPSS and Arc GIS simulation tool at temporal and spatial scale. The FFT (fast Fourier Transform) is used to identify the Flood Trends in different regions of China from 1980 to 2009. Along with the Advancement in technology Alferi et al [9] had focused his research towards numerical weather forecasting based early Flood warning system which is based on Ensemble Flood Predictions. The use of metrological data sets such as (COSMO) for the 30 years entire period is used to drive discharge climatology and also results in early warning threshold. According to Alferi et al [9] the gamma probability distribution is the best method for the quantative analysis of water flow, which is an optimal method in Flood warning system. The Future Analysis in Flood Prediction for an event based approach using time window of appropriate duration improves the threshold analysis, also in terms of False alarm rates and hit rates. Ahmad et al [10] had proposed the integrated Model of Flood prediction using GIS based wireless sensor networks for calculating the impact of Flood damage during the monsoon regions especially in the areas of Sind Pakistan. The rain fall data during the last few years had been collected from Pakistan Metrological Department (web source). Relevant data and information had been used to predict warning relevant to Floods, Thunder storms and rainfall using ARC GIS Simulation tool. Ahmad et al [10] had also proposed the mathematical Model for Flood prediction which also provides the impact of Flood disaster in the selected region. The hydrograph in the Figure 1 provides in deep detail about the observed discharge peak level within different warning levels in the Flood affected areas. The Operational methods of Research focuses on Numerical weather prediction also known as ensemble Prediction Systems. One of the major short falls of using EPS for Flood forecasting system designed for hydrological application is limited to low frequency of Floods in disaster effected region.

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Fig 1: Example of an ensemble hydrographs for historical Flood events [11]

According to Neal et al [12] space time sampling strategy is used in Flood forcasting using wireless sensor nodes.The proposed Flood forcasting Model forcasts using Webbased GIS. Flood forcasting Model takes the Real time data from Gateway node deployed in the region of Flood effected area. Gateway node is connected to several sensor nodes working in peer to peer architecture. Neal at al [12] had also developed a technique named as Ensemble Transform Kalman Filter which is used to estimate the potential value of unceratnities in upcoming Flood forcast.

Fig 2: Example of deployment of Sensor location w.r.t. sensor location within a specified time [12]

The Flood forcasting Model takes the Real time data from Gateway node deployed in the region of Flood effected area. Gateway node is connected to several sensor nodes working in peer to peer architecture. Neal et al [12] had also developed a technique named as Ensemble Transform Kalman Filter which is used to estimate the potential

5 value of unceratnities in upcoming Flood forcast. The Figure 2 provides the three dimensional view related to the deployment of sensor nodes with Q possible measurements at eight different locations with respect to the signal variance intensity measured in meters over the specified coverage range. The adaptive sampling results are based on five different targeted time events which strongly represents the working of sensors with in specified time interval along with its signal variance intensity at discrete events. Hugs et al [13] had designed an embedded computing platform named as grid stix based WSN. 2.2 Particle Swarm Optimization Technique (PSO) The most latest technique used by researchers now a days is PSO and artifical neural fuzzy inference system for various problems like [14-19]. These techniques has being used for extreeme flood prediction in case of emergency circumstances. The Particle swarm optimization technique has been adopted by scientists and researchers in hydrological Modeling. PSO is a group based stochastic technique falls under the category of Evolutionary algorithms and soft computing developed by Kennedy and Eberhart in 1995. In particle swarm optimization technique there is a group of multiple random particles laocated with in specified position and moves through the entire space to search for potential solution. Along with the solution identification these particles also learns with in a group using neural network approach [20]. According to Eberhart and shi (2001) the emphasis of PSO algorithm focuses on the best fitness postion of each particle within the entire space named as personal best (pbest) and global best (gbest) collectively depending upon the movement acceleration towards the next particle velocity with in the hyper space. The are two major euations which are used in particle swarm optimization technique. The Firest euqation is known as movement equation which is described as follows.

Pr esentlocation  Pr eviouslocation  Vi t ...........(1)

The Present location of the particles despends upon the previous location of the particles within a specfied vector space along with the individual velocity of particles in the specific interval of time. The Second Equation is known as velocity update equation which can be described as follows. Vi  wVi 1  C1 * rand () * ( Pbest  preslocation) 

C 2 * rand () * ( gbest  preslocation)...........................(2) The velocity update equation descibes the change in velovity for the particles with in the entire movement space known as gbest and pbest. In the equation (2)

Vi

is the initial

velocity of the particles, t is the time interval for the movement of particles with-in a hyper space,

Vi 1

is the previous velocity, random() is the random number value for

C

C

example (0,1,2,3..........) and 1 and 2 are the acceleration of the particles with in the entire space. The strengths of the research work is based on application of PSO algorithms specially in artifical neural networks for enhancing the learning process. Along with this the Authors had also proposed a unique technique for calibrating the daily rainfall runoff model named as PSONN (Particle swarm optimization neural neural networks). The input parameters for the PSONN model are temperature, moisturecontent and evaporation. 2.3 Artificial Neural Based fuzzy inference system (ANFIS) ANFIS technique is a multilayer feed forward back propogation netwok which is capable to forward the weighted connections from input to output layers [21]. The ANN Model identifies a set of parameters which serve as a basis of IF –then fuzzy rules based on apporiate member functions. The Sugneo inference system has been adopted which

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Ahmed et. al. provides efficient mathematical modeling and optimization technique. The first order sugeno fuzzy model can be mathematically expressed as follows.

Rule1 : IfXisA1andYisB1 , thenf1  p1 x  q1 y  r1......(3) Rule2 : IfXisA2 andYisB2 , thenf 2  p 2 x  q2 y  r2 ....(4) The above mentioned Rule based equations depends on the output function f corresponds to the input vector value x and y. The values p, q and r represents the constant quantaties.

Fig 3: Graphical Representation of five layer Feed forward ANFIS system [20]

The Figure 3 demonstrates the graphical representation of ANFIS system which consists of Input layer mentioned as Layer 1, hidden layer mentioned as Layer 2,3 and 4 and finally the output layer mentioned as Layer 5. The Most common Neural Network Model is classified as MLP (Multi layer preception )neural network.The major goal of this supervised network is to map the input into output.

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Proposed mathematical model for flood forcasting

The following section describes the proposed model for Flood forcasting based on artifical neural fuzzy inference system. 3.1 Adpative neuro-fuzzy inference system algorithm

7 The selection of parameters for flood forcasting using the wireless sensor networks plays a vital role in tranning the neural network using ANFIS learning algorithm.

Fig 4: Block Digram representation of ANFIS evaluation learning based model

The selection of parameters for flood forcasting is very important factor in flood forcasting and disaster risk analysis. The key parameters for flood forcasting are humidity, rainfall, tempertaure, pressure and wind speed.The parameters are selected using wireless sensor network based architecture. The next step is to categorize the flood forcasting parameters based on flood intensity and range.The heavy intensity rainfall in milimeters for the long period of time, for example one week creates a potential impact of flood in rivers, streams and water basins specially in rural areas. The next step is to apply ANFIS technique on the selected set of parameters. The ANFIS system in this research scenario emphazies on rainfall paramter which results in increased water level intensity with in the region of intest. The next step is to apply fuzzy if-then rules based on resulting mathematical equations as mentioned in equation 1 and 2. In this step we have also design the mathematical representation of the function f1 and f2 based on the fuzzy sets. Finally the equation for the overall output is represented which is the product of the resulting function and the overall output of the system. It is also possible to train the neural network learning algorithm based on adaptive neuro fuzzy inference system. 3.1.1 Mathematical representation of adpative neuro-fuzzy inference system algorithm The following euqations describe in deep detail about the mathematical representation of artifical neuro fuzzy inference system [23]. R f = rainfall

Wl  waterlevel IfR f isA1andWL isB1 , then f1  p( RF )  q(WL )  r1....................................(5) Internationa Conference on Data and Information Sceicnes (ICDIS-2017) Springer Series: Lecture Notes in Networks and Systems

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Ahmed et. al. IfR f isA2 andWL isB2 , then

f 2  p( RF )  q(WL )  r2 ....................................(6)

WhereA1 , A2 andB1 , B2 arefuzysets Layer 1:

Oi   Ai ( RF )

Layer 2:

Wi   Ai ( RF ) *  Bi (WL )

Layer 3:

Wi 

Layer 4:

Oi  Wi fi  Wi ( Pi ( RF )  Qi (WL )  ri )

Layer 5:

1

w1 w1  w2

i=1,2,............. i=1,2,..................

4

Overall Output = Wi f i i

The output functions for the equations depends on rainfall and water fall parameters multiplied by the constant values. The Layer 1 focusses on the output function, layer 2 and layer 3 describes about the weighted functions and finally layer 4 describes about the output function for the product of weight and input function. The input parameters for the proposed mathematical model are rain fall intensity will be measured in milimeters and water lavel will be calculated in cubic feet per meters. 3.2 Calculating flood disaster risk ratioby using probability density function The probability of flood disaster risk ratio is calculated by using Bayesian decision theory which is based on the following set of parameters. TH  Temperature(High)

TL  Temperature(Low) TC  TH  TL (Temperaturechange) R  Ra inf allIntensity(mm) H  Humidity(%) e  predictionerror   predictioncons tan t According to basian decision theory probability density model can be applied to calculate the humidity and rainfall. The equations can be derived on the basis of generic formula derived from [17] mentioned as follows.

P(TC | R) 

P(TC | H ) 

P( R | TC ) P(TC ) .......................(9) P( R)

P( H | TC ) P(TC ) .....................(10) P( H )

The linear regression function based on probability density model can be described as follows.

g ( t | R, H )  R t  H ..................................(11) Finally the regression function r can be defined as follws

r  g ( x)   .....................................................(12)

Where epslon is the prediction error.

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Proposed Methodology for Flood Prediction

The proposed methodology is designed on the basis of following set of parameters. The selected parameters for flood forecasting are rainfall intensity which is measured in millimeters, temperature which is calculated in centigrade, humidity level along with air

9 pressure is calculated in Pascals. Finally the dam overflow factor which is dependent on rainfall intensity is calculated in water reservoir level in feet’s. The decision is represented by diamond. The decision step is based on the selection of input parameters.

Fig 5: Proposed methodology based on input parameters for flood prediction

If the input parameters are successfully determined there are multiple possible ways which are mentioned as follows. • The first option is to select the numerical values of input parameters and use the parameters an an input to ARC GIS simulation tool. • The next step is to calculate the flood frequency and runoff process using ARC GIS simulation tool. • Finally the results are generated on the basic of graphs. • The second option is to select the numerical values of input parameters and apply the values on the proposed mathematical model to generate the results including graphs. • Finally perform the comparative analysis of both the techniques and perpare the rescue authorities for emergency operations.

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Results and Discussion

The data has been obtained from Pakistan metrological department during the month of September 2014 [22] . The structure of data is based on designed capacity of water storage level of River Indus. It also depends upon the actual in Flow and out-flow of water level based on reservoir elevation. The comparative analysis had been obtained on the basis of numerically weather prediction normally classified as rainfall intensity.

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Ahmed et. al. The Figure 6 provides the graphical information related to actual Inflow and out Flow depending on water reservoir level. The redline indicated the maximum ouflow of water depending upon its storage capacity and blue line indicates the moderate level of actual inflow depending on resivor storage designed capacity.

Fig 6: Graphical Representation of Actual Inflow and out Flow grph

Fig 7: Graphical Representation of very high Intensity range Inflow and out Flow The Figure No 7 provides the graphical representation of very high Intensity of actual Inflow and out flow depending on multiple numerical ranges.The numerical ranges along x-axis and y-axis provides the information related to the the water level measured in cubic centimeters. It has been observed from the simulation results that the ouflow of water as mentioned in red colour increases depending on the storage reservoir level elevation and capacity to store the water in maximum cubic centimeters.

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Conclusion and Future Work

This research paper investigates how to build standard rules and regulations to be followed in case of any flood emergency circumstances. Majority of authors have discussed about the applications of GIS in flood circumstance. The proposed flood hazard model focuses on the comparative analysis of the calculated and estimated results. On the basis of these results we can predict the intensity of flood disaster in the specific region of interest. The proposal is based on a combination of GIS, Wireless Sensor Network and Artificial Nuro Fuzzy Inference System.

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