Urban flooding is highly uncertain, so the use of probabilistic approaches in early flood warning is encouraged. While well‐established 1‐D/2‐D hydrodynamic sewer models do exist, their deterministic nature and long computational time undermine their applicability for real‐time urban flood nowcasting. Aiming at meeting the needs of fast and probabilistic flood modeling, a new hybrid modeling method integrating a suite of lumped hydrological models and logistic regression is proposed. The lumped models are configured using graph theory techniques, based on the sewer system's topology and characteristics, to account for the spatial heterogeneity and physical processes and properties. The logistic regression models are calibrated to lumped models' results and Yes/No flooding information. Due to its conceptual and data‐driven nature, the hybrid model makes fast probabilistic flood predictions at manhole locations. In two case studies, the results showed that when incorporating most dominant physical processes and properties in the model setup, the hybrid model can achieve up to 86% accuracy for flood warnings issued at 50% probability, with 96% computation saving, compared with a traditional 1‐D hydrodynamic model. When such a detailed model is in place, the hybrid model is set up easily, but the accuracy could be further increased when historical flooding observations are considered.

中文翻译：

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快速概率城市雨洪预报的混合模型

城市洪水是高度不确定的，因此鼓励在早期洪水预警中使用概率方法。尽管确实存在完善的1D / 2D流体动力学下水道模型，但它们的确定性和较长的计算时间破坏了它们在实时城市洪水临近预报中的适用性。为了满足快速概率洪水建模的需求，提出了一种新的混合建模方法，该方法将集总水文模型和逻辑回归相结合。基于下水道系统的拓扑结构和特征，使用图论技术对集总模型进行配置，以解决空间异质性以及物理过程和属性的问题。将逻辑回归模型校准为集总模型的结果和“是/否”洪水信息。由于其概念和数据驱动特性，混合模型可以在沙井位置进行快速概率洪水预测。在两个案例研究中，结果表明，在模型设置中纳入最主要的物理过程和属性时，与使用混合模型相比，混合模型以50％的概率发出洪水警报时，可以达到高达86％的精度，节省96％的计算量。传统的一维流体动力学模型。如果有这样一个详细的模型，则可以轻松建立混合模型，但是考虑历史洪水观测结果，则可以进一步提高准确性。与传统的一维流体动力学模型相比，可节省96％的计算量。如果有这样一个详细的模型，则可以轻松建立混合模型，但是考虑历史洪水观测结果，则可以进一步提高准确性。与传统的一维流体动力学模型相比，可节省96％的计算量。如果有这样一个详细的模型，则可以轻松建立混合模型，但是考虑历史洪水观测结果，则可以进一步提高准确性。