MaxFloodCast: Ensemble Machine Learning Model for Predicting Peak Inundation Depth And Decoding Influencing Features
It provides timely, interpretable flood predictions for decision-makers and emergency managers, though it is incremental as it applies existing ensemble methods to flood data.
The study tackled predicting peak flood inundation depths using the MaxFloodCast ensemble machine learning model, achieving an average R-squared of 0.949 and RMSE of 0.61 ft on unseen data, validated against Hurricane Harvey and Storm Imelda.
Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average R-squared of 0.949 and a Root Mean Square Error of 0.61 ft on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model's interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively.