Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation
Precipitation is a key driving factor of hydrologic modeling for impact studies. However, there are challenges due to limited long-term data availability and complex parameterizations of existing stochastic weather generators (SWGs) due to spatiotemporal uncertainty. We introduced state-of-the-art...
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| 主要な著者: | , , , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Elsevier Ltd.
2024
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| 主題: | |
| オンライン･アクセス: | http://ir.unimas.my/id/eprint/44860/2/Implementing%20generative.pdf http://ir.unimas.my/id/eprint/44860/ https://www.sciencedirect.com/science/article/pii/S1364815223002827 https://doi.org/10.1016/j.envsoft.2023.105896 |
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