Enhancing generalizability of data-driven urban flood models by incorporating contextual information
Year: 2024
Type: article
Abstract: . Fast urban pluvial flood models are necessary for a range of applications, such as near real-time flood nowcasting or processing large rainfall ensembles for uncertainty analysis. Data-driven models can help overcome the long computational time of traditional flood simulation models, and the state-of-the-art models have shown promising accuracy. Yet the lack of generalizability of urban pluvial flood data-driven models to both terrain and rainfall events still limits their application. These models usually adopt a patch-based framework to overcome multiple bottlenecks, such as data availability and computational and memory constraints. However, this approach does not incorporate contextual information of the terrain surrounding the small image patch (typically 256 m x 256 m). We propose a new deep-learning model that maintains the high-resolution information of the local patch and incorporates a larger context to increase the visual field of the model with the aim of enhancing the generalizability of urban pluvial flood data-driven models. We trained and tested the model in the city of Zurich (Switzerland), at a spatial resolution of 1 m, for 1-hour rainfall events at 5 min temporal resolution. We demonstrate that our model can faithfully represent flood depths for a wide range of rainfall events, with peak rainfall intensities ranging from 42.5 mm h-1 to 161.4 mm h-1. Then, we assessed the model’s terrain generalizability in distinct urban settings, namely Luzern (Switzerland) and Singapore. The model accurately identifies locations of water accumulation, which constitutes an improvement compared to other deep-learning models. Using transfer learning, the model was successfully retrained in the new cities, requiring only a single rainfall event to adapt the model to new terrains while preserving adaptability across diverse rainfall conditions. Our results indicate that by incorporating contextual terrain information into the local patches, our proposed model effectively generates high-resolution urban pluvial flood maps, demonstrating applicability across varied terrains and rainfall events.
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,725
Funders: National Science Foundation, National Research Foundation, National University of Singapore, Nanyang Technological University, Branco Weiss Fellowship – Society in Science, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Eidgenössische Technische Hochschule Zürich, Singapore University of Technology and Design
OpenAlex ID: W4405553061