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.

提出了一种基于物理水动力学模拟训练的机器学习模型 MaxFloodCast，可提供高效和可解释的洪水淹没深度预测。通过对未见数据达到 0.949 的平均 R 平方和 0.61 英尺的均方根误差，验证了其在预测洪峰淹没深度方面的可靠性。模型的可解释性有助于决策者提供重要信息以制定洪水缓解策略，优先考虑关键设施区域，并检查其他水域降雨如何影响一个区域的洪水暴露情况。MaxFloodCast 模型能够准确可解释地预测淹没深度，同时显著降低计算时间，从而更有效地支持应急响应工作和洪水风险管理。