An LCZ-Based Machine Learning Reveals Differences in Coastal High-Density Urban Flood Risk: Enhancing Interpretability and Simplifying Morphology

Urban pluvial flooding is intensifying under rapid urbanization and climate change, yet most data-driven assessments underrepresent the role of urban morphology. We tentatively treat the Local Climate Zone (LCZ) scheme as a standardized morphological proxy rather than a purely hydrological variable. This study introduces an innovative analytical framework that integrates LCZ-based morphological indicators into a LightGBM machine learning model, enhanced by Shapley Additive Explanations (SHAP) for improved interpretability. Using data derived from Guangzhou and Shenzhen, we constructed two model scenarios: a baseline model employing traditional socio-environmental variables and an enhanced model incorporating LCZ typologies. The enhanced model demonstrated a substantial improvement in predictive accuracy, particularly in Guangzhou, where LCZ-related factors contributed over 30% to the model's importance, with a higher relative contribution rate than standalone 3D building metrics. Compared with conventional land-use classification, LCZ produced a markedly finer-grained urban form. Besides, SHAP analyses further revealed distinct threshold effects associated with specific land coverage levels. By coupling standardized morphology with interpretable machine learning, this framework is scalable across cities and provides actionable guidance for adaptive planning It prioritizes infrastructure improvements—such as street renovations and permeable upgrades—in areas exhibiting the highest morphological sensitivity.