Discriminator‐Guided Generative Adversarial Networks for Urban Flood Prediction
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| Pubblicato in: | Water Resources Research vol. 61, no. 11 (Nov 1, 2025) |
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| Autore principale: | |
| Altri autori: | , , |
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John Wiley & Sons, Inc.
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| Accesso online: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 0043-1397 | ||
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| 024 | 7 | |a 10.1029/2025WR040510 |2 doi | |
| 035 | |a 3269471456 | ||
| 045 | 0 | |b d20251101 | |
| 084 | |a 107315 |2 nlm | ||
| 100 | 1 | |a Li, Zhufeng |u Centre for Water Systems, University of Exeter, Exeter, UK | |
| 245 | 1 | |a Discriminator‐Guided Generative Adversarial Networks for Urban Flood Prediction | |
| 260 | |b John Wiley & Sons, Inc. |c Nov 1, 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Flood modeling is crucial in flood management as it can provide early warnings and support informed decision‐making on mitigation and adaptation strategies. However, it remains challenging to provide accurate flood predictions in real time using hydrodynamic flood models due to high computational demands. This study presents a new discriminator‐guided Generative Adversarial Neural Networks (GANs) model for two‐dimensional, high‐resolution urban flood prediction. Compared with the traditional GANs, the role of the discriminator is re‐defined by modifying its structure and loss function, enabling pixel‐wise discrimination based on errors, thereby better meeting the requirements of high‐resolution flood prediction. The proposed model is tested on the case study of Exeter, which covers an area of 27 km2 with a spatial resolution of 2 m, compared with the baseline models of Pix2Pix and U‐Net. The proposed model can accurately predict the water depths across historical and design rainfall events, achieving an average root mean square error of 0.044 m and Critical Success Index of 0.754, demonstrating the generalization capability on unseen rainfall events. The proposed model significantly improves computational efficiency and offers a viable solution for spatiotemporal flood prediction in real‐time, providing informed decision‐making for urban flood management. | |
| 653 | |a Dams | ||
| 653 | |a Flood management | ||
| 653 | |a Deep learning | ||
| 653 | |a Floods | ||
| 653 | |a Discriminators | ||
| 653 | |a Rainfall | ||
| 653 | |a Water depth | ||
| 653 | |a Neural networks | ||
| 653 | |a Generative adversarial networks | ||
| 653 | |a Computer applications | ||
| 653 | |a Spatial discrimination | ||
| 653 | |a Flood control | ||
| 653 | |a Design rainfall | ||
| 653 | |a Flood forecasting | ||
| 653 | |a Precipitation | ||
| 653 | |a Predictions | ||
| 653 | |a Spatial resolution | ||
| 653 | |a Structure-function relationships | ||
| 653 | |a Decision making | ||
| 653 | |a Flood predictions | ||
| 653 | |a Flood models | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Fu, Zeyu |u Department of Computer Science, University of Exeter, Exeter, UK | |
| 700 | 1 | |a Li, Qian |u Centre for Water Systems, University of Exeter, Exeter, UK | |
| 700 | 1 | |a Fu, Guangtao |u Centre for Water Systems, University of Exeter, Exeter, UK | |
| 773 | 0 | |t Water Resources Research |g vol. 61, no. 11 (Nov 1, 2025) | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3269471456/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3269471456/fulltext/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3269471456/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |