Modeling inundation patterns resulting from compound flooding induced by tropical cyclones presents significant challenges due to the complex interplay of drivers and features affecting inundation mechanisms. This study introduces a machine learning framework designed to optimize the prediction of inundation depth by balancing model performance, computational costs and efforts for input data retrieval. Starting from a comprehensive, physics-informed identification of the potential explanatory variables, including features that capture local flood dynamics, as well as topological and geographical characteristics, the proposed methodology leverages a feature selection process based on permutation importance, which emphasizes the reduction in the number of inputs to streamline the modeling process without compromising accuracy. The framework has been tested using Hurricane Harvey as a case study. The analysis revealed performance in inundation depth prediction comparable to that of traditional hydrodynamic models available in the literature. Results demonstrated that focusing on the most informative features improves both model performance and efficiency, thus highlighting the need for careful feature selection for region-specific implementation of data-driven approaches for inundation depth prediction.

中文翻译：

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探索热带气旋导致沿海洪水的复合性质：机器学习框架


由于影响洪水机制的驱动因素和特征之间存在复杂的相互作用，因此对热带气旋诱发的复合洪水导致的洪水模式进行建模提出了重大挑战。本研究引入了一个机器学习框架，旨在通过平衡模型性能、计算成本和输入数据检索工作来优化洪水泛滥深度的预测。从对潜在解释变量的全面、物理信息识别开始，包括捕获局部洪水动态的特征，以及拓扑和地理特征，所提出的方法利用基于排列重要性的特征选择过程，强调减少输入数量，以简化建模过程而不影响准确性。该框架已使用飓风哈维作为案例研究进行了测试。分析显示，洪水深度预测的性能与文献中可用的传统水动力模型相当。结果表明，专注于信息量最大的特征可以提高模型性能和效率，从而突出了在特定地区实施数据驱动方法进行洪水深度预测时仔细选择特征的必要性。