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.

Highlights

  • Various natural disasters occur frequently worldwide, among which flooding is the most common and devastating (Stefanidis and Stathis, 2013)

  • 265 et al, 2013), which was shown as follow: I ln Ni / Si where I is the weight of factor class i; Ni is the number of floods in class i; Si is the number of pixel class i; N is the number of floods in the whole study area; S is the number of pixels in the entire study area

  • For the prediction-rate curve of support vector machine (SVM) (Fig. 4b), the area under ROC (AUC) is 0.934, indicating that the SVM model has a good prediction effectiveness. Both the AUC values of the success-rate curve and the prediction-rate curve of SVM were greater than 0.9, which demonstrates that the results obtained in this study using the SVM model are scientific and reliable

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Summary

Introduction

Various natural disasters occur frequently worldwide, among which flooding is the most common and devastating (Stefanidis and Stathis, 2013). Both society and ecosystems suffer from the profound effects of floods. In the Belt and Road region, 1483 floods occurred from 2000 to 2020, accounting for 44.9% of the total floods around the world based on the statistic from the Emergency Disasters Database (EMDAT, CRED, http://www.emdat.be/). The construction of “the Belt and the Road” involves a large number of infrastructure and major engineering projects in transportation, communication and energy. These projects are always planned and deployed

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