Matthew Garcia

Cheng-Chun Lee, Lipai Huang, Federico Antolini et al.

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.

Lipai Huang, Federico Antolini, Ali Mostafavi et al.

High-resolution flood probability maps are instrumental for assessing flood risk but are often limited by the availability of historical data. Additionally, producing simulated data needed for creating probabilistic flood maps using physics-based models involves significant computation and time effort, which inhibit its feasibility. To address this gap, this study introduces Precipitation-Flood Depth Generative Pipeline, a novel methodology that leverages generative machine learning to generate large-scale synthetic inundation data to produce probabilistic flood maps. With a focus on Harris County, Texas, Precipitation-Flood Depth Generative Pipeline begins with training a cell-wise depth estimator using a number of precipitation-flood events model with a physics-based model. This cell-wise depth estimator, which emphasizes precipitation-based features, outperforms universal models. Subsequently, the Conditional Generative Adversarial Network (CTGAN) is used to conditionally generate synthetic precipitation point cloud, which are filtered using strategic thresholds to align with realistic precipitation patterns. Hence, a precipitation feature pool is constructed for each cell, enabling strategic sampling and the generation of synthetic precipitation events. After generating 10,000 synthetic events, flood probability maps are created for various inundation depths. Validation using similarity and correlation metrics confirms the accuracy of the synthetic depth distributions. The Precipitation-Flood Depth Generative Pipeline provides a scalable solution to generate synthetic flood depth data needed for high-resolution flood probability maps, which can enhance flood mitigation planning.