Flood vulnerability assessment using artificial neural networks in Muar Region, Johor Malaysia

9th IGRSM International Conference and Exhibition on Geospatial & Remote Sensing

地球科学;计算机科学

Wahab, A.M.^1 ; Muhamad Ludin, A.N.^2

Department of Urban and Town Planning, Faculty of Built Environment, Universiti Teknologi Malaysia, Malaysia^1

Centre for Innovative Planning and Development, Faculty of Built Environment, Universiti Teknologi Malaysia, Malaysia^2

关键词: Disaster risk reductions;    Flood risk management;    Flood vulnerabilities;    Lavenberg-marquardt;    Multi layer perceptron;    Performance Model;    Root mean square errors;    Sampling location;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/169/1/012056/pdf
DOI  :  10.1088/1755-1315/169/1/012056

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

The measurement of Flood Vulnerability Assessment (FVA) is essential because it is the key towards disaster risk reduction as well as improving the community. Several existing techniques can be used to measure FVA, however, these processes are challenging, such as in determining the weighting factors. Therefore, this study attempts to apply Artificial Neural Networks (ANN) technique in the assessment of FVA because it can determine the weight between both input and output data through the network training process. The ANN technique has been widely applied in flood modelling; however, the application of ANN in FVA has not been explored extensively, mainly due to difficulties in obtaining real data. The purpose of this study is to assess the capability of Multilayer Perceptron (MLP) ANN technique with Lavenberg-Marquardt back-propagation algorithm in determining FVA in Muar region, Johor, Malaysia. The ANN architecture for this study is 9-N-N-1, i.e., nine nodes in the input layer, two hidden layers with N nodes, and one node for the output layer. In the training process of the network, 30 sampling locations with a 9x9 window were selected in the floodplain and non-flooded areas. Meanwhile, another 30 sampling locations were used to test the network in order to measure the performance model. Then, the trained network was used to generate FVA map for the study area. The result of the performance model shows the root mean square error is 0.0035 has been made between input data with target data. The findings of this study showed that the ANN technique can precisely approximate the FVA without predetermined weighting factors, but it depends on the target data used. The result of this study can also be used as a tool to assist decision makers in preparing national and local plans which take into consideration flood risk management.