DRUM: Diffusion-based runoff model for probabilistic flood forecasting
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| Publicat a: | arXiv.org (Dec 16, 2024), p. n/a |
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| Autor principal: | |
| Altres autors: | , , , , , , , , |
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Cornell University Library, arXiv.org
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| Accés en línia: | Citation/Abstract Full text outside of ProQuest |
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|---|---|---|---|
| 001 | 3145904455 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3145904455 | ||
| 045 | 0 | |b d20241216 | |
| 100 | 1 | |a Ou, Zhigang | |
| 245 | 1 | |a DRUM: Diffusion-based runoff model for probabilistic flood forecasting | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 16, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Reliable flood forecasting remains a critical challenge due to persistent underestimation of peak flows and inadequate uncertainty quantification in current approaches. We present DRUM (Diffusion-based Runoff Model), a generative AI solution for probabilistic runoff prediction. DRUM builds up an iterative refinement process that generates ensemble runoff estimates from noise, guided by past meteorological conditions, present meteorological forecasts, and static catchment attributes. This framework allows learning complex hydrological behaviors without imposing explicit distributional assumptions, particularly benefiting extreme event prediction and uncertainty quantification. Using data from 531 representative basins across the contiguous United States, DRUM outperforms state-of-the-art deep learning methods in runoff forecasting regarding both deterministic and probabilistic skills, with particular advantages in extreme flow (0.1%) predictions. DRUM demonstrates superior flood early warning skill across all magnitudes and lead times (1-7 days), achieving F1 scores near 0.4 for extreme events under perfect forecasts and maintaining robust performance with operational forecasts, especially for longer lead times and high-magnitude floods. When applied to climate projections through the 21st century, DRUM reveals increasing flood vulnerability in 47.8-57.1% of basins across emission scenarios, with particularly elevated risks along the West Coast and Southeast regions. These advances demonstrate significant potential for improving both operational flood forecasting and long-term risk assessment in a changing climate. | |
| 653 | |a Flood forecasting | ||
| 653 | |a Noise generation | ||
| 653 | |a Runoff | ||
| 653 | |a Deep learning | ||
| 653 | |a Basins | ||
| 653 | |a Noise prediction | ||
| 653 | |a Weather forecasting | ||
| 653 | |a Hydrologic models | ||
| 653 | |a Uncertainty | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Flood predictions | ||
| 700 | 1 | |a Congyi Nai | |
| 700 | 1 | |a Pan, Baoxiang | |
| 700 | 1 | |a Pan, Ming | |
| 700 | 1 | |a Shen, Chaopeng | |
| 700 | 1 | |a Jiang, Peishi | |
| 700 | 1 | |a Liu, Xingcai | |
| 700 | 1 | |a Tang, Qiuhong | |
| 700 | 1 | |a Li, Wenqing | |
| 700 | 1 | |a Zheng, Yi | |
| 773 | 0 | |t arXiv.org |g (Dec 16, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3145904455/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.11942 |