RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN >> 2023, Vol. 32 >> Issue (8): 1710-1723.doi: 10.11870/cjlyzyyhj202308014

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Flood Risk Assessment Based on Machine Learning Algorithms:A Case Study of Yichang City

WANG De-yun1,2,ZHANG Lu-dan1,WU Qi1,GUO Hai-xiang1,2,KE Xiao-ling1,LV Xin-biao3    

  1. (1. School of Economics and Management,China University of Geosciences,Wuhan 430074,China;2. Laboratory of Natural Disaster Risk Prevention and Emergency Management, China University of Geosciences,Wuhan 430074,China;3. Institute for Advanced Study, China University of Geosciences,Wuhan 430074,China)
  • Online:2023-08-20 Published:2023-08-23

Abstract: In recent years, flooding events happened frequently and severely affected people’s lives and property. Objective and accurate risk assessment is vital for urban flood risk prevention and emergency management. This paper takes the flood event happened in Yichang City in late June 2020 as an example to analyze the influencing factors of urban flooding and conduct risk assessment. Firstly, based on RS remote sensing technology, we extracted the inundation area of water bodies before and after flooding using Sentinel II radar images and conducted random sampling. Then, initially selected 16 basic indicators from four different perspectives: flood-causing, flood-pregnant, flood-bearing and recovery capability. Finally, the XGBoost model was used to assign weights to the optimized indicators and carry out risk assessment. The assessment results show that: (1) among the factors influencing flood risk in Yichang, the influence of topography and river distribution > socio-economic factors > meteorological factors; (2) the scope of high-risk areas is closely related to the distribution of major river systems such as the Yangtze River, Qingjiang River, Fuzhan River, Huangbai River and Yuyang River. The relative departments in Yichang should be highly sensitive to the water level of these river systems and make the essential emergency management measures; (3) the low-risk to medium-risk areas account for 71.8% of the total area of the region, but only contain 8% of the flood hazard sites; while the high-risk area only accounts for 7.32% of the total area, but contains 81.33% of the flood hazard sites, indicating a high flooding intensity in this area; (4) the assessment results of the model are verified to be consistent with the actual situation in Yichang City by using small-scale historical disaster data, the verification results show that 72% of the validation points fall into the high to higher-risk areas, and up to 92% of the validation points fall into the medium-high risk areas. This study solves the difficulty of quantitative assessment of flood risk at fine scales, and provides a useful reference for urban flood risk management, disaster prevention and mitigation efforts and regional planning.

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