Rainstorm Disaster Risk Assessment and Influence Factors Analysis in the Yangtze River Delta, China

Menghua Deng; Zhiqi Li; Feifei Tao

doi:10.3390/ijerph19159497

Rainstorm Disaster Risk Assessment and Influence Factors Analysis in the Yangtze River Delta, China

Int J Environ Res Public Health. 2022 Aug 2;19(15):9497. doi: 10.3390/ijerph19159497.

Authors

Menghua Deng^{1

2}, Zhiqi Li¹, Feifei Tao³

Affiliations

¹ Business School, Hohai University, Nanjing 210098, China.
² Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China.
³ College of Computer and Information, Hohai University, Nanjing 210098, China.

Abstract

Rainstorm disasters have had a serious impact on the sustainable development of society and the economy. However, due to the complexity of rainstorm disasters, it is difficult to measure the importance of each indicator. In this paper, the rainstorm disaster risk assessment framework was systematically proposed based on the disaster system theory and a system of corresponding indicators was established. Furthermore, the genetic algorithm optimized projection pursuit and XGBoost were coupled to assess the rainstorm disaster risk and to measure the relative importance of each indicator. Finally, the Yangtze River Delta was taken as the case study area. The results show that: the rainstorm disaster risk in the eastern and southeast is higher than those in the central and northwest of the Yangtze River Delta; the total precipitation from June to September and the top ten indicators contribute 9.34% and 74.20% to the rainstorm disaster risk assessment results, respectively. The results can provide references for decision makers and are helpful for the formulation of rainstorm adaptation strategies.

Keywords: XGBoost; Yangtze River Delta; projection pursuit; rainstorm disaster; risk assessment.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

China
Disasters*
Risk Assessment / methods
Rivers*
Sustainable Development

Grants and funding

This research was funded by the Fundamental Research Funds for the Central Universities, grant number. B200201032; the National Natural Science Foundation of China, grant number. 42001250; Changzhou Introduction and Cultivation of Leading Innovative Talents Program, grant number. CQ20210095; Jiangsu Water Conservancy Science and Technology Foundation, grant number. 2020014.