Assessment of flood susceptibility mapping using support vector machine, logistic regression and their ensemble techniques in the Belt and Road region

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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Flood susceptibility mapping using GIS-based support vector machine and particle swarm optimization: A case study in Uttarakhand (India)

2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT) ◽

10.1109/icccnt.2017.8204182 ◽

2017 ◽

Cited By ~ 10

Author(s):

Shruti Sachdeva ◽

Tarunpreet Bhatia ◽

A. K. Verma

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Particle Swarm ◽

Susceptibility Mapping ◽

Support Vector ◽

Swarm Optimization ◽

Flood Susceptibility ◽

Flood Susceptibility Mapping

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Assessment of Flood Susceptibility Using Support Vector Machine in the Belt and Road Region

10.5194/nhess-2021-80 ◽

2021 ◽

Author(s):

Jun Liu ◽

Junnan Xiong ◽

Weiming Cheng ◽

Yi Li ◽

Yifan Cao ◽

...

Keyword(s):

Support Vector Machine ◽

Southeast Asia ◽

South Asia ◽

Rate Curve ◽

Support Vector ◽

Flood Susceptibility ◽

Svm Model ◽

Belt And Road ◽

High Flood ◽

The Belt And Road

Abstract. Floods have occurred frequently all over the world. During 2000–2020, nearly half (44.9 %) of global floods occurred in the Belt and Road region because of its complex geology, topography, and climate. However, the degree of flood susceptibility of each sub-region and country in the Belt and Road region remains unclear. Here, based on 11 flood condition factors, the support vector machine (SVM) model was used to generate a flood susceptibility map. Then, we introduced the flood susceptibility comprehensive index (FSCI) for the first time to quantify the flood susceptibility levels of the sub-regions and countries in the Belt and Road region. The results reveal the following. (1) The SVM model used in this study has an excellent accuracy, and the AUC values of the success-rate curve and prediction-rate curve were higher than 0.9 (0.917 and 0.934 respectively). (2) The areas with the highest and high flood susceptibility account for 12.22 % and 9.57 % of the total study area respectively, and these areas are mainly located in the southeastern part of Eastern Asia, almost the entirely of Southeast Asia and South Asia. (3) Of the seven sub-regions in the Belt and Road region, Southeast Asia is most susceptible to flooding and has the highest FSCI (4.49), followed by South Asia. (4) Of the 66 countries in this region, 16 of the countries have the highest flood susceptibility level (normalized FSCI > 0.8) and 5 countries (normalized FSCI > 0.6) have a high flood susceptibility level. These countries need to pay more attention to flood mitigation and management. The above findings provide useful information for decision-making in flood management in the Belt and Road region. In the future study, higher quality flood points, and climate change factors should be considered.

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