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.

翻译：及时、准确且可靠的信息在洪水事件中对决策者、应急管理人员和基础设施运营者至关重要。本研究提出了一个机器学习模型MaxFloodCast，该模型基于哈里斯县基于物理的水动力模拟训练，能够提供高效且可解释的洪水淹没深度预测。在未见数据上，模型平均R平方达到0.949，均方根误差为0.61英尺，证明其在预测峰值洪水淹没深度方面具有可靠性。经飓风哈维和风暴伊梅尔达验证，MaxFloodCast展现出支持近实时洪泛区管理和应急行动的潜力。模型的可解释性有助于决策者获取关键信息，以制定洪水缓解策略、优先关注关键基础设施区域，并研究其他流域降雨如何影响某区域的洪水暴露程度。MaxFloodCast模型在显著降低计算时间的同时，实现了准确且可解释的淹没深度预测，从而更有效地支持应急响应工作和洪水风险管理。