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

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.

Urban nature-based solutions planning for biodiversity outcomes: human, ecological, and artificial intelligence perspectives

“…The few existing AI flood models are based on a simple blackbox approach and therefore lack generalization (Bentivoglio et al, 2021). However, recent advancements have been made with several AI methods being introduced for rapid urban flood prediction (Burrichter et al, 2023), including a site-based model using Long-Short Term Memory (LSTM), a 1-D model employing Graph-Neural Networks (GNN), and a 2-D model utilizing Generative Adversarial Networks (GAN). A significant challenge with these models is the lack of simulated data, often necessitating a comprehensive output from physically based hydrodynamic models for accurate 1-D and 2-D urban flood predictions.…”

A Temporal Fusion Transformer Model to Forecast Overflow from Sewer Manholes during Pluvial Flash Flood Events

“…On the other hand, other methods focus on generating predictions down to the manhole level [25][26][27][28][29]. In [30], an integrated consideration of overflow and flooding area predictions was also performed. In addition to precipitation information, the developed model also considers a forecast of overflow from manholes as an additional load to predict the flooded areas for the upcoming time steps.…”

A Feature-Informed Data-Driven Approach for Predicting Maximum Flood Inundation Extends

“…Thus, the hydrodynamic model can be replaced by the data-driven model. In recent years, several studies have been presented using this methodology [9][10][11][12][13][14][15]. Some of these studies use artificial neural networks (ANNs).…”

“…Other studies however make use of different types and architectures of data-driven models such as convolutional neural networks (CNN) [10,[12][13][14][15]. CNNs are specifically designed for processing structured grid-like data, such as images.…”

“…The latest studies, besides the CNN structure, used physical information like, for example, the digital elevation model or roughness values as additional inputs to the datadriven model [13,15,19]. The idea is the additional data provided will give the data-driven model a deeper knowledge about the flood inundation to improve performance.…”