Analysis of affected population vulnerability to rainstorms and its induced floods at county level: A case study of Zhejiang Province, China. (1st June 2022)

• Record Type: Journal Article
• Authors: Liao, Xinli; Xu, Wei; Zhang, Junlin; Qiao, Yu; Meng, Chenna
• Is Part Of: International journal of disaster risk reduction. Volume 75(2022)
• Publication Date: 2022-06-01
• Subjects: Rainstorms and its induced floods -- Vulnerability -- Random forest regression -- Response curve -- County level; Emergency management -- Periodicals; Risk management -- Periodicals; Disaster relief -- Periodicals; Hazard mitigation -- Periodicals; 363.34
• DOI: 10.1016/j.ijdrr.2022.102976
• Languages: English
• ISSNs: 2212-4209

Abstract

Abstract: The impacts of rainstorms and its induced floods (RAIF) are substantial and rising, and the coastal regions in southeastern China may suffer more because of the frequent occurrence of RAIF. Therefore, research on the vulnerability of the affected population to RAIF is vital. This study presents an assessment framework for vulnerability in Zhejiang Province, China, at county level. Based on data related to loss records, precipitation, population, economy, topography and hydrology, relatively important variables were first selected by random forest regression and agglomerative hierarchical clustering to avoid multicollinearity and over-fitting. And then we established and validated a vulnerability model and analyzed the importance score and response curve of each variable. The results indicated the following: (1) The counties suffering more from RAIF were mostly distributed in the southeast and west of Zhejiang Province. Approximately 1.42 million people were affected per year. (2) The R 2 of the vulnerability model based on random forest regression was 0.41, and the largest multiple-day rainfall was the most import driver of the population affected by RAIF. (3) The response curve of the largest multiple-day rainfall showed a trend of first increasing and then stabilizing; GDP per capita first decreased sharply and tended to become stable; population first increased, then decreased and showed an increasing trend again. An innovative aspect of this work was the use of machine learning to analyze the vulnerability and the non-linear relationships between variables and the affected population, and these results may help policymakers develop suitable mitigation strategies against RAIF.