Session Info
Identifying flash flood hotspots with explainable machine learning using urban features
Pluvial flash floods are fast-moving hazards and causes significant disruptions in urban areas. This study presents interpretable machine learning models for predicting urban flash flood hotspots based on intertwined land and built environment features. Various features related to land and built environment characteristics are constructed using diverse datasets, and the occurrences of flash floods are captured using crowdsource data from the events. Using these features and datasets, the flash flood hotspots of cities are predicted with two ensemble models based on decision trees. The results demonstrate that the models can achieve good accuracy in identifying flooded/non-flooded locations. The model interpretation results indicate that land features related to hydrological and topological features have greater impacts on flash flood risk, than built environment features. The data-driven machine learning models presented in this study provide a useful tool for predicting flash flood hotspots based on the intertwined features of land and the built environment in cities to enable nowcasting and proactive monitoring of flash flood hotspots for emergency response and also inform integrated urban design and development towards flash flood risk reduction.