Urban flood susceptibility zonation mapping using evidential belief function, frequency ratio and fuzzy gamma operator models in GIS: a case study of Greater Mumbai, Maharashtra, India

2020 ◽  
pp. 1-26 ◽  
Author(s):  
Veerappan Ramesh ◽  
Sayed Sumaira Iqbal
Keyword(s):  
Frequency Ratio ◽  
Belief Function ◽  
Urban Flood ◽  
Evidential Belief Function ◽  
Flood Susceptibility ◽  
Gamma Operator ◽  
Fuzzy Gamma Operator

scholarly journals Groundwater productivity potential mapping using frequency ratio and evidential belief function and artificial neural network models: focus on topographic factors

2018 ◽  
Vol 20 (6) ◽  
pp. 1436-1451 ◽  
Author(s):  
Jeong-Cheol Kim ◽  
Hyung-Sup Jung ◽  
Saro Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Frequency Ratio ◽  
Groundwater Resources ◽  
Specific Capacity ◽  
Belief Function ◽  
Neural Network Models ◽  
Evidential Belief Function ◽  
Artificial Neural ◽  
Productivity Potential

Abstract This study analysed groundwater productivity potential (GPP) using three different models in a geographic information system (GIS) for Okcheon city, Korea. Specifically, we have used variety topography factors in this study. The models were based on relationships between groundwater productivity (for specific capacity (SPC) and transmissivity (T)) and hydrogeological factors. Topography, geology, lineament, land-use and soil data were first collected, processed and entered into the spatial database. T and SPC data were collected from 86 well locations. The resulting GPP map has been validated in under the curve analysis area using well data not used for model training. The GPP maps using artificial neural network (ANN), frequency ratio (FR) and evidential belief function (EBF) models for T had accuracies of 82.19%, 81.15% and 80.40%, respectively. Similarly, the ANN, FR and EBF models for SPC had accuracies of 81.67%, 81.36% and 79.89%, respectively. The results illustrate that ANN models can be useful for the development of groundwater resources.

scholarly journals A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7653 ◽  
Author(s):  
Mahyat Shafapour Tehrany ◽  
Lalit Kumar ◽  
Farzin Shabani
Keyword(s):  
Support Vector Machine ◽  
Statistical Analysis ◽  
Belief Function ◽  
Susceptibility Mapping ◽  
Support Vector ◽  
Ensemble Technique ◽  
Evidential Belief Function ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping ◽  
The Individual

In this study, we propose and test a novel ensemble method for improving the accuracy of each method in flood susceptibility mapping using evidential belief function (EBF) and support vector machine (SVM). The outcome of the proposed method was compared with the results of each method. The proposed method was implemented four times using different SVM kernels. Hence, the efficiency of each SVM kernel was also assessed. First, a bivariate statistical analysis using EBF was performed to assess the correlations among the classes of each flood conditioning factor with flooding. Subsequently, the outcome of the first stage was used in a multivariate statistical analysis performed by SVM. A highest prediction accuracy of 92.11% was achieved by an ensemble EBF-SVM—radial basis function method; the achieved accuracy was 7% and 3% higher than that offered by the individual EBF method and the individual SVM method, respectively. Among all the applied methods, both the individual EBF and SVM methods achieved the lowest accuracies. The reason for the improved accuracy offered by the ensemble methods is that by integrating the methods, a more detailed assessment of the flooding and conditioning factors can be performed, thereby increasing the accuracy of the final map.

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