Unraveling Nonlinear and Spatially Heterogeneous Impacts of Urban Pluvial Flooding Factors in a Hill-Basin City Using Geographically Explainable Artificial Intelligence: A Case Study of Changsha

Ziqiang He, Yu Chen (), Qimeng Ning, Bo Lu (), Shixiong Xie and Shijie Tang
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Ziqiang He: School of Architecture and Urban Planning, Hunan City University, Yiyang 413000, China
Yu Chen: School of Architecture and Urban Planning, Hunan City University, Yiyang 413000, China
Qimeng Ning: School of Architecture and Urban Planning, Hunan City University, Yiyang 413000, China
Bo Lu: School of Architecture and Urban Planning, Hunan City University, Yiyang 413000, China
Shixiong Xie: School of Civil and Environmental Engineering, Hunan University of Technology, Zhuzhou 412007, China
Shijie Tang: Shanghai Academy of Fine Arts, Shanghai University, Shanghai 200444, China

Sustainability, 2025, vol. 17, issue 21, 1-23

Abstract: The factors influencing urban pluvial flooding in cities with complex topography, such as hill–basin systems, are highly nonlinear and spatially heterogeneous due to the interplay between rugged terrain and intensive human activities. However, previous research has predominantly focused on plain, mountainous, and coastal cities. As a result, the waterlogging mechanisms in hill–basin areas remain notably understudied. In this study, we developed a geographically explainable artificial intelligence (GeoXAI) framework integrating Geographical Machine Learning Regression (GeoMLR) and Geographical Shapley (GeoShapley) values to analyze nonlinear impacts of flooding factors in Changsha, a typical hill–basin city. The XGBoost model was employed to predict flooding risk (validation AUC = 0.8597, R 2 = 0.8973), while the GeoMLR model verified stable nonlinear driving relationships between factors and flooding susceptibility (test set R 2 = 0.7546)—both supporting the proposal of targeted zonal regulation strategies. Results indicated that impervious surface density (ISD), normalized difference vegetation index (NDVI), and slope are the dominant drivers of flooding, with each exhibiting distinct nonlinear threshold effects (ISD > 0.35, NDVI < 0.70, Slope < 5°) that differ significantly from those identified in plain, mountainous, or coastal regions. Spatial analysis further revealed that topography regulates flooding by controlling convergence pathways and flow velocity, while vegetation mitigates flooding through enhanced interception and infiltration, showing complementary effects across zones. Based on these findings, we proposed tailored zonal management strategies. This study not only advances the mechanistic understanding of urban waterlogging in hill–basin regions but also provides a transferable GeoXAI framework offering a robust methodological foundation for flood resilience planning in topographically complex cities.

Keywords: hill-basin; urban pluvial flooding; GeoXAI; nonlinear threshold; spatial heterogeneity; zonal management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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