A Systematic Modular Approach for the Coupling of Deep-Learning-Based Models to Forecast Urban Flooding Maps in Early Warning Systems

Abstract

Deep learning (DL) approaches to forecast precipitation and inundation areas in the short-term forecast horizon have up until now been treated as independent research problems from the model development perspective. However, for the urban hydrology area, the coupling of these models is necessary in order to forecast the upcoming inundation area maps and is, therefore, of the utmost importance for successful flood risk management. In this paper, three deep-learning-based models are coupled in a systematic modular approach with the aim to analyze the performance of this model chain in an operative setup for urban pluvial flooding nowcast: precipitation nowcasting with an adapted version of the NowcastNet model, the forecast of manhole overflow hydrographs with a Seq2Seq model, and the generation of a spatiotemporal sequence of inundation areas in an urban catchment for the upcoming hour with an encoder–decoder model. It can be concluded that the forecast quality still largely depends on the accuracy of the precipitation nowcasting model. With the increasing development of DL models for both precipitation and flood nowcasting, the presented modular approach for model coupling enables the substitution of individual blocks for better and newer models in the model chain without jeopardizing the operation of the flooding forecast system.