Predicting peak inundation depths with a physics informed machine learning model

Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that 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 documentclass[12pt]{minimal} usepackage{amsmath} usepackage{wasysym} usepackage{amsfonts} usepackage{amssymb} usepackage{amsbsy} usepackage{mathrsfs} usepackage{upgreek} setlength{oddsidemargin}{-69pt} egin{document}$$R^2$$end{document}R2 of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical 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.

在洪水事件期间，及时、准确和可靠的信息对于决策者、应急管理人员和基础设施运营商至关重要。这项研究表明，一个提出的机器学习模型MaxFloodCast，基于哈里斯县基于物理的流体动力学模拟进行训练，能够提供高效且可解释的洪水淹没深度预测。在未见过的数据上，其平均$R^2$达到0.949，均方根误差为0.61英尺（0.19米），证明在预测洪水峰值淹没深度方面是可靠的。通过与飓风哈维和热带风暴伊梅尔达进行验证，MaxFloodCast显示出在支持近实时洪泛区管理和应急行动方面的潜力。该模型的可解释性有助于决策者提供关键信息，为洪水缓解策略提供依据，确定具有关键设施区域的优先级，并研究其他流域的降雨如何影响一个地区的洪水暴露情况。MaxFloodCast模型能够进行准确且可解释的淹没深度预测，同时显著减少计算时间，从而更有效地支持应急响应工作和洪水风险管理。