A Spatiotemporal Deep Learning Approach for Urban Pluvial Flood Forecasting with Multi-Source Data

Water, vol. 15, pp. 1760

Abstract

This study presents a deep-learning-based forecast model for spatial and temporal prediction of pluvial flooding. The developed model can produce the flooding situation for the upcoming time steps as a sequence of flooding maps. Thus, a dynamic overview of the forthcoming flooding situation is generated to support the decision of crisis management actors. The influence of different input data, data formats, and model setups on the prediction results was investigated. Data from multiple sources were considered as follows: precipitation information, spatial information, and an overflow forecast. In addition, models with different layers and network architectures such as convolutional layers, graph convolutional layers, or generative adversarial networks (GANs) were considered and evaluated. The data required to train and test the models were generated using a coupled hydrodynamic 1D/2D model. The model setup with the inclusion of all available input variables and an architecture with graph convolutional layers presented, in general, the best results in terms of root mean square error (RMSE) and critical success index (CSI). The prediction results of the final model showed a high agreement with the simulation results of the hydrodynamic model, with drastic reductions in computation time, making this model suitable for integration into an early warning system for pluvial flooding.

Authors 5

1. Benjamin Burrichter corresponding

   Ruhr West University of Applied Sciences

   Affiliation as printed

   Institute of Civil Engineering, University of Applied Sciences Ruhr West, 45479 Mülheim an der Ruhr, Germany

2. Julian Hofmann Aachen Institute for Hydraulic Engineering and Water Resources Management

   RWTH Aachen University

   Affiliation as printed

   Institute for Hydraulic Engineering and Water Resources Management, RWTH Aachen University, 57074 Aachen, Germany

3. Juliana Koltermann da Silva

   Ruhr West University of Applied Sciences

   Affiliation as printed

   Institute of Civil Engineering, University of Applied Sciences Ruhr West, 45479 Mülheim an der Ruhr, Germany

4. André Niemann

   University of Duisburg-Essen

   Affiliation as printed

   Institute for Hydraulic Engineering and Water Resources Management, University of Duisburg-Essen, 45141 Essen, Germany

5. Markus Quirmbach

   Ruhr West University of Applied Sciences

   Affiliation as printed

   Institute of Civil Engineering, University of Applied Sciences Ruhr West, 45479 Mülheim an der Ruhr, Germany