Exploring the compound nature of coastal flooding by tropical cyclones: A machine learning framework

Modeling inundation patterns resulting from compound flooding induced by tropical cyclones presents significant challenges due to the complex interplay of drivers and features affecting inundation mechanisms. This study introduces a machine learning framework designed to optimize the prediction of inundation depth by balancing model performance, computational costs and efforts for input data retrieval. Starting from a comprehensive, physics-informed identification of the potential explanatory variables, including features that capture local flood dynamics, as well as topological and geographical characteristics, the proposed methodology leverages a feature selection process based on permutation importance, which emphasizes the reduction in the number of inputs to streamline the modeling process without compromising accuracy. The framework has been tested using Hurricane Harvey as a case study. The analysis revealed performance in inundation depth prediction comparable to that of traditional hydrodynamic models available in the literature. Results demonstrated that focusing on the most informative features improves both model performance and efficiency, thus highlighting the need for careful feature selection for region-specific implementation of data-driven approaches for inundation depth prediction.