Ensemble learning analysis of influencing factors on the distribution of urban flood risk points: a case study of Guangzhou, China - Zhao, Juchao; Wang, Jin; Abbas, Zaheer; Yang, Yao; Zhao, Yaolong (Frontiers in Earth Science, 2023)
Urban waterlogging is a major natural disaster in the process of urbanization. It is of great significance to carry out the analysis of influencing factors and susceptibility assessment of urban waterlogging for related prevention and control. However, the relationship between urban waterlogging and different influencing factors is often complicated and nonlinear. Traditional regression analysis methods have shortcomings in dealing with high-dimensional nonlinear issues. Quantifying the relative contributions of such factors to urban waterlogging is usually challenging, whereas ensemble learning algorithms are a potential solution to this challenging issue. This paper proposed a technical framework for quantitative analysis and susceptibility assessment on influencing factors of urban waterlogging based on the Gradient Boosting Decision Tree (GBDT) model in a case study in Guangzhou city, China. Main factors and indicators affecting urban waterlogging in terrain and topography, impervious surface, vegetation coverage, drainage facilities, rivers, etc., were selected for the GBDT model. furthermore, the relative contribution of each influencing factor to urban waterlogging was implemented quantifiably, and the urban waterlogging susceptibility assessment was conducted subsequently. The results show that drainage density, impervious surface, and NDVI are the most important influencing factors resulting in rainstorm waterlogging, with a total contribution of 85.34%. The overall distribution of urban waterlogging susceptibility shows a characteristic of "high in the southwest and low in the northeast", in which the high-susceptibility areas are mainly distributed in Yuexiu District (34%), followed by Liwan District (22%) and Haizhu District (20%). To mitigate the impact of frequent urban flooding disasters, future measures should focus on strengthening drainage networks, such as optimizing impervious surface spatial patterns, controlling construction activities in high-risk areas, and preventing excessive development of green spaces.