Real-time simulation of flood inundation helps to mitigate the catastrophic effects on human lives by facilitating emergency evacuations. Traditional two-dimensional (2D) physics-based hydrodynamic models, though accurate, require significant computational time, thereby rendering them unsuitable for such real-time applications. To address this limitation, we developed Random Forest (RF) models as surrogate hydrodynamic models for predicting maximum flood depth and velocity under complex fluvial conditions with backwater effects. These models integrate hydrological parameters, such as upstream discharge, physical catchment characteristics, to enhance predictive accuracy and generalizability. A comprehensive assessment revealed that the inclusion of physical characteristics increased the prediction accuracy of RF models by 1.72 times and 2.60 times for depth and velocity models with root mean square error of 0.494 m and 0.148 m/s respectively, compared to baseline models. Furthermore, the RF models required only 1.5 %–4 % (for minor flood event and major flood event respectively) of the computational time needed by hydrodynamic models. With its ability to understand complex flooding scenarios with high prediction accuracy and computing efficiency, the proposed RF models have demonstrated great potential for real-time flood inundation modelling. Efforts in this direction to improve the real-time flood inundation predictions may greatly aid the decision makers for undertaking emergency evacuations during catastrophic flood events.

中文翻译：

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使用随机森林进行实时洪水泛滥模拟的代理机器学习模型


洪水泛滥的实时模拟有助于通过促进紧急疏散来减轻对人类生活的灾难性影响。传统的基于二维 （2D） 物理场的流体动力学模型虽然准确，但需要大量的计算时间，因此不适合这种实时应用。为了解决这一限制，我们开发了随机森林 （RF） 模型作为替代水动力学模型，用于预测具有回水效应的复杂河流条件下的最大洪水深度和速度。这些模型整合了水文参数，例如上游流量、物理集水区特征，以提高预测准确性和泛化性。综合评估显示，与基线模型相比，包含物理特性使深度和速度模型的预测精度提高了 1.72 倍和 2.60 倍，均方根误差分别为 0.494 m 和 0.148 m/s。此外，RF 模型只需要水动力模型所需计算时间的 1.5 %–4 % （分别为小洪水事件和大洪水事件）。凭借其理解复杂洪水情景的能力，具有很高的预测精度和计算效率，所提出的 RF 模型在实时洪水泛滥建模方面显示出巨大的潜力。朝着这个方向努力改进实时洪水泛滥预测可能会极大地帮助决策者在灾难性洪水事件期间进行紧急疏散。