Study on the Risk of Urban Population Exposure to Waterlogging in Huang-Huai Area Based on Machine Learning Simulation Analysis—A Case Study of Xuzhou Urban Area

Shuai Tong, Jiuxin Wang, Jiahui Qin, Xiang Ji () and Zihan Wu
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Shuai Tong: School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
Jiuxin Wang: School of Architecture and Design, China University of Mining and Technology, Xuzhou 221116, China
Jiahui Qin: School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Xiang Ji: School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China
Zihan Wu: School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China

Land, 2025, vol. 14, issue 5, 1-25

Abstract: With the acceleration of climate change and the increase of extreme rainfall, the risk of flooding has intensified in the Huang-Huai region, which is often hit by floods. Urban water accumulation is a complicated process, and the hydrological simulation analysis is highly accurate, but it is time-consuming and laborious. Machine learning is becoming an important new method because of its ability to analyze large areas with high precision. In this paper, a simulation analysis method based on machine learning is constructed by selecting 13 disaster factors, and the waterlogging point in Xuzhou city is predicted successfully. The following conclusions are found: (1) Among the five machine learning models, CatBoost has the highest accuracy rate, reaching 81.67%. (2) Temperature, elevation, and rainfall are relatively important influencing factors of waterlogging. (3) Machine learning can discover water accumulation areas that are easily overlooked except for the built-up areas. (4) The results of the coupling analysis show that the exposure risk of the population exposed to rainwater in the old urban area, the southern area, and the northwestern area is relatively high. This research is of great significance for reducing the risk of exposure to rain and flooding and promoting the safety and sustainable development of cities.

Keywords: machine learning; CatBoost; waterlogging risk; population exposure; Xuzhou city (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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