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Urban Flood Modeling and Forecasting With Deep Neural Operator and Transfer Learning

MCML Authors

Qingsong Xu

→ Group Xiaoxiang Zhu
Data Science in Earth Observation

Xiaoxiang Zhu

Prof. Dr.

Principal Investigator

Data Science in Earth Observation

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.

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