Xi Huang , Linhan Yang , GuiCheng Yang , Jiufeng Li , Luo Liu , Yilun Liu , Xianzhe Tang
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引用次数: 0
Abstract
Amid climate change and urban expansion, urban floods (UFs) have become a significant concern. Effective UF management requires two key elements: (1) assessing susceptibility to identify flood-prone hotspots and (2) analyzing factor contributions to pinpoint primary drivers. While research on urban flood susceptibility (UFS) is well-established, studies on factor contributions usually limited to the simple application without fully evaluating their alignment with UF characteristics. This study addresses this gap by examining Shenzhen, a rapidly urbanizing city, and applying various regression methods (linear, multiple machine learning (ML), and Shapley Additive exPlanations (SHAP)) to evaluate flood-driving factors. Through accuracy assessments and environmental considerations, we identify the most suitable methods for this analysis. Our findings include: (1) Random Forest (RF)-based UFS assessment reveals significant spatial heterogeneity, with higher UFS in central-western areas and lower risks in the southeast; (2) the most contributing factors in Shenzhen exhibit non-linear and non-parametric effects on UF, supporting the use of RF; (3) we found substantial spatial heterogeneity in factor contributions, necessitating SHAP analysis to capture local variations and provide tailored management insights. Thus, this study integrates methodological rigor with urban environmental context, enhancing factor contribution analysis for UF and supporting effective management strategies.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.