Preprints
https://doi.org/10.5194/nhess-2021-80
https://doi.org/10.5194/nhess-2021-80
25 May 2021
 | 25 May 2021
Status: this discussion paper is a preprint. It has been under review for the journal Natural Hazards and Earth System Sciences (NHESS). The manuscript was not accepted for further review after discussion.

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

Jun Liu, Junnan Xiong, Weiming Cheng, Yi Li, Yifan Cao, Yufeng He, Yu Duan, Wen He, and Gang Yang

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Jun Liu, Junnan Xiong, Weiming Cheng, Yi Li, Yifan Cao, Yufeng He, Yu Duan, Wen He, and Gang Yang

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-80', Anonymous Referee #1, 28 May 2021
    • AC1: 'Reply on RC1', Jun Liu, 29 Jun 2021
  • RC2: 'Comment on nhess-2021-80', Anonymous Referee #2, 23 Jun 2021
    • AC2: 'Reply on RC2', Jun Liu, 29 Jun 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-80', Anonymous Referee #1, 28 May 2021
    • AC1: 'Reply on RC1', Jun Liu, 29 Jun 2021
  • RC2: 'Comment on nhess-2021-80', Anonymous Referee #2, 23 Jun 2021
    • AC2: 'Reply on RC2', Jun Liu, 29 Jun 2021
Jun Liu, Junnan Xiong, Weiming Cheng, Yi Li, Yifan Cao, Yufeng He, Yu Duan, Wen He, and Gang Yang
Jun Liu, Junnan Xiong, Weiming Cheng, Yi Li, Yifan Cao, Yufeng He, Yu Duan, Wen He, and Gang Yang

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Latest update: 26 May 2024
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Short summary
The Belt and Road region has frequent flooding, however, the spatial pattern of flood susceptibility here is not yet clear. To this end, this study used support vector machine to generate a flood susceptibility map of this region based on a novel method of non-flood point selection. More importantly, we introduced the flood susceptibility comprehensive index (FSCI) to quantitatively analyze the flood susceptibility levels of 7 sub-regions and 66 countries in the study area.
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