Rapid Flood Inundation Forecast Using Fourier Neural Operator

• URL: http://arxiv.org/abs/2307.16090v1
• Date: Sat, 29 Jul 2023 22:49:50 GMT
• Title: Rapid Flood Inundation Forecast Using Fourier Neural Operator
• Authors: Alexander Y. Sun, Zhi Li, Wonhyun Lee, Qixing Huang, Bridget R. Scanlon, Clint Dawson
• Abstract summary: Flood inundation forecast provides critical information for emergency planning before and during flood events. High-resolution hydrodynamic modeling has become more accessible in recent years, however, predicting flood extents at the street and building levels in real-time is still computationally demanding. We present a hybrid process-based and data-driven machine learning (ML) approach for flood extent and inundation depth prediction.
• Score: 77.30160833875513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flood inundation forecast provides critical information for emergency planning before and during flood events. Real time flood inundation forecast tools are still lacking. High-resolution hydrodynamic modeling has become more accessible in recent years, however, predicting flood extents at the street and building levels in real-time is still computationally demanding. Here we present a hybrid process-based and data-driven machine learning (ML) approach for flood extent and inundation depth prediction. We used the Fourier neural operator (FNO), a highly efficient ML method, for surrogate modeling. The FNO model is demonstrated over an urban area in Houston (Texas, U.S.) by training using simulated water depths (in 15-min intervals) from six historical storm events and then tested over two holdout events. Results show FNO outperforms the baseline U-Net model. It maintains high predictability at all lead times tested (up to 3 hrs) and performs well when applying to new sites, suggesting strong generalization skill.

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