An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility

Urban flooding risks, often overlooked by conventional methods, can be profoundly affected by city configurations. However, explainable Artificial Intelligence could provide insights into how urban configurations affected urban flooding. This study, taking entered on Shenzhen City, deploys an XGBoost, integrating SHapley Additive exPlanation and Partial Dependency Plots, to assess how urban morphology influences urban flooding susceptibility. The models and strategies presented in this study aimed to adapt to extreme storms from the perspective of city spatial configuration planning. The findings underscore the varying impact of disaster variables on urban flooding, with morphological attributes becoming…

• GM
  Guangzhou Municipal Science and Technology Project

  Award: 202201010431
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2023A1515030158