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 G...
| Główni autorzy: | , , , , |
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| Format: | Artykuł |
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Elsevier Ltd
2024
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| _version_ | 1825939263214059520 |
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| author | Ji, Hong Kang Mirzaei, Majid Lai, Sai Hin Dehghani, Adnan Dehghani, Amin |
| author_facet | Ji, Hong Kang Mirzaei, Majid Lai, Sai Hin Dehghani, Adnan Dehghani, Amin |
| author_sort | Ji, Hong Kang |
| collection | UPM |
| description | 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 Generative Adversarial Network (GAN) as a data-driven multi-site SWG and synthesized extensive hourly precipitation over 30 years at 14 stations. These samples were then fed into an hourly-calibrated SWAT model for streamflow generation. Results showed that the well-trained GAN improved rainfall data by accurately representing spatiotemporal distribution of raw data rather than simply replicating its statistical characteristics. GAN also helped display authentic spatial correlation patterns of extreme rainfall events well. We concluded that GAN offers a superior spatiotemporal distribution of raw data compared to conventional methods, thus enhancing the reliability of flood frequency evaluations. |
| first_indexed | 2024-04-09T03:51:40Z |
| format | Article |
| id | upm.eprints-105818 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-04-09T03:51:40Z |
| publishDate | 2024 |
| publisher | Elsevier Ltd |
| record_format | dspace |
| spelling | upm.eprints-1058182024-03-29T03:28:42Z http://psasir.upm.edu.my/id/eprint/105818/ Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation Ji, Hong Kang Mirzaei, Majid Lai, Sai Hin Dehghani, Adnan Dehghani, Amin 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 Generative Adversarial Network (GAN) as a data-driven multi-site SWG and synthesized extensive hourly precipitation over 30 years at 14 stations. These samples were then fed into an hourly-calibrated SWAT model for streamflow generation. Results showed that the well-trained GAN improved rainfall data by accurately representing spatiotemporal distribution of raw data rather than simply replicating its statistical characteristics. GAN also helped display authentic spatial correlation patterns of extreme rainfall events well. We concluded that GAN offers a superior spatiotemporal distribution of raw data compared to conventional methods, thus enhancing the reliability of flood frequency evaluations. Elsevier Ltd 2024 Article PeerReviewed Ji, Hong Kang and Mirzaei, Majid and Lai, Sai Hin and Dehghani, Adnan and Dehghani, Amin (2024) Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation. Environmental Modelling and Software, 172. art. no. 105896. pp. 1-14. ISSN 1364-8152; ESSN: 1873-6726 10.1016/j.envsoft.2023.105896 |
| spellingShingle | Ji, Hong Kang Mirzaei, Majid Lai, Sai Hin Dehghani, Adnan Dehghani, Amin Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation |
| title | Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation |
| title_full | Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation |
| title_fullStr | Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation |
| title_full_unstemmed | Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation |
| title_short | Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation |
| title_sort | implementing generative adversarial network gan as a data driven multi site stochastic weather generator for flood frequency estimation |
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