Urban flood modeling and forecasting with deep neural operator and transfer learning

Qingsong Xu, Leon Frederik De Vos, Yilei Shi, Nils Rüther, Axel Bronstert, Xiao Xiang Zhu,
Urban flood modeling and forecasting with deep neural operator and transfer learning,
Journal of Hydrology,
Volume 661, Part B,
2025,
133705,
ISSN 0022-1694,
https://doi.org/10.1016/j.jhydrol.2025.133705.
(https://www.sciencedirect.com/science/article/pii/S0022169425010431)
Abstract: Physics-based models provide accurate flood modeling but are limited by their dependence on high-quality data and computational demands, particularly in complex urban environments. Machine learning-based surrogate models like neural operators present a promising alternative; however, their practical application in urban flood modeling remains challenges, such as insufficient feature representation, high memory demands, and limited transferability. To address these challenges, this study introduces a deep neural operator (DNO) and a transfer learning-based DNO for fast, accurate, resolution-invariant, and cross-scenario urban flood forecasting. The DNO features an enhanced Fourier layer with skip connections for improved memory efficiency, alongside a deep encoder-decoder framework and an urban-embedded residual loss to enhance modeling effectiveness. The transfer learning-based DNO further integrates a fine-tuning-based approach for efficient cross-scenario forecasting in the target domain and a domain adaptation-based strategy for continuous learning across diverse domains. The fine-tuning-based DNO enables rapid adaptation to target domains, while the domain adaptation-based DNO mitigates knowledge forgetting from the source domain. Experimental results demonstrate that the proposed DNO significantly outperforms existing neural solvers using a comprehensive urban flood benchmark dataset, particularly in predicting high water depths and exhibiting exceptional zero-shot downscaling performance for high-resolution forecasting. Moreover, the fine-tuning-based DNO enhances transferability for cross-scenario urban flood forecasting, while the domain adaptation-based DNO achieves accurate flood predictions in both source and target domains, even with limited labeled target data. Through the combination of these ML methods and the benchmark dataset, a practical tool is established for effective, cross-scenario, and downscaled spatiotemporal urban flood forecasting.
Keywords: Urban flood forecasting; Machine learning; Deep neural operator (DNO); Fine-tuning-based DNO; Domain adaptation-based DNO; Urban flood benchmark dataset