This paper proposes a hybrid modelling approach for early urban flood warning and forecasting purpose. The hybrid model structure proposed combines a deterministic sewer model with a probabilistic logistic regression model. By varying the sewer model structures from complex hydrodynamic models to simple conceptual models, different hybrid models are constructed in order to accommodate different levels of available knowledge and data about the urban hydrological system. The hybrid models are optimized using crowdsourced flooding records. They can predict the probability of flooding at the urban sub-catchment scale. The proposed methodology is tested for two cases in Antwerp, Belgium. Promising results show its potential in making fast and reliable urban flood predictions. Reasonable predictions are made even by the simplest model form, indicating the method could work also for sub-catchments with limited information. It also shows that the proposed methodology allows identifying the most dominant hydrological processes explaining urban flooding.

本文提出了一种用于城市早期预警和预报的混合建模方法。提出的混合模型结构结合了确定性下水道模型和概率逻辑回归模型。通过将下水道模型结构从复杂的水动力模型改变为简单的概念模型，可以构建不同的混合模型，以适应有关城市水文系统的不同级别的可用知识和数据。混合模型使用众包洪水记录进行了优化。他们可以预测城市次流域规模洪水的可能性。在比利时的安特卫普，针对两种情况对提出的方法进行了测试。有希望的结果显示出其在进行快速可靠的城市洪水预报中的潜力。即使是最简单的模型形式，也可以做出合理的预测，表示该方法也可用于信息量有限的子汇水区。它还表明，所提出的方法论可以确定解释城市洪水的最主要的水文过程。