A Hybrid Model for Fast and Probabilistic Urban Pluvial Flood Prediction

Abstract

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.