Assessment of Flood Susceptibility Using Support Vector Machine in the Belt and Road Region
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.
Jun Liu et al.
Jun Liu et al.
Jun Liu et al.
Viewed (geographical distribution)
10 citations as recorded by crossref.
- Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study over Adyar watershed, India S. Sundaram et al. 10.1007/s11356-023-26467-7
- Evaluation of pre- and post-fire flood risk by analytical hierarchy process method: a case study for the 2021 wildfires in Bodrum, Turkey O. Yilmaz et al. 10.1007/s11355-023-00545-x
- Flood vulnerability mapping and urban sprawl suitability using FR, LR, and SVM models A. Youssef et al. 10.1007/s11356-022-23140-3
- Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping S. Seydi et al. 10.3390/rs15010192
- Comparative study of convolutional neural network (CNN) and support vector machine (SVM) for flood susceptibility mapping: a case study at Ras Gharib, Red Sea, Egypt A. Youssef et al. 10.1080/10106049.2022.2046866
- Flood susceptibility modeling of the Karnali river basin of Nepal using different machine learning approaches S. Duwal et al. 10.1080/19475705.2023.2217321
- Flood Image Classification using Convolutional Neural Networks O. Adetunji et al. 10.53982/ajerd.2023.0602.11-j
- GIS-based hybrid machine learning for flood susceptibility prediction in the Nhat Le–Kien Giang watershed, Vietnam H. Nguyen 10.1007/s12145-022-00825-4
- Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia A. Al-Areeq et al. 10.3390/rs14215515
- Flood susceptibility prediction using MaxEnt and frequency ratio modeling for Kokcha River in Afghanistan A. Qasimi et al. 10.1007/s11069-023-06232-2