A Hybrid Model for Fast and Probabilistic Urban Pluvial Flood Prediction

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 nowcastin...

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Bibliographic Details
Published in:Water resources research Vol. 56; no. 6
Main Authors: Li, Xiaohan, Willems, Patrick
Format: Journal Article
Language:English
Published: Washington John Wiley & Sons, Inc 01.06.2020
Subjects:
Accuracy
Computation
Computer applications
Computer simulation
Computing time
crowdsource flood reports
data‐driven model
Drainage
Flood forecasting
Flood predictions
Flood warnings
Flooding
Floods
Graph theory
Heterogeneity
Historic floods
hybrid model
Hydrodynamic models
Hydrodynamics
Hydrologic models
Hydrology
logistic regression
Manholes
mathematical theory
Model accuracy
Modelling
Nowcasting
Patchiness
prediction
probability
Probability theory
Properties
Rain
Rainfall
Regression analysis
Regression models
River discharge
Sewer systems
Spatial heterogeneity
spatial variation
Statistical analysis
Topology
Urban areas
Urban drainage
urban flooding
water
ISSN:0043-1397, 1944-7973
Online Access:Get full text
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Summary: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. Plain Language Summary Forecasting urban flooding in real time requires a set of future rainfall scenarios and an urban drainage model. This procedure needs to be not only fast, so that the forecasts can be quickly updated, but also informative about the uncertainties in the forecasts. State‐of‐the‐art urban drainage models could simulate floods at street level based on physical processes, but the level of details make them slow and deterministic. Therefore, creating a model by reducing its physical complexity and complementing it with data‐driven methods is promising to meet the need for real‐time urban flood forecasting. The current study adopts such a hybrid model to predict flooding probabilities at detailed intracity locations. Key Points A hybrid model is proposed to predict flooding probabilities at manholes using spatial‐heterogeneous conceptual modeling and logistic regression In the two case studies, the hybrid model achieved up to 86% accuracy with 96% computation saving, compared with original 1‐D HD models The model is considered useful as a surrogate/supplementary tool for real‐time urban flood nowcasting
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ISSN:0043-1397
1944-7973
DOI:10.1029/2019WR025128