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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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Main Authors: Ji, Hong Kang, Majid, Mirzaei, Lai, Sai Hin, Adnan, Dehghani, Amin, Dehghani
Format: Article
Language:English
Published: Elsevier Ltd. 2024
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access: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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spelling my.unimas.ir.448602024-05-27T03:03:39Z http://ir.unimas.my/id/eprint/44860/ Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation Ji, Hong Kang Majid, Mirzaei Lai, Sai Hin Adnan, Dehghani Amin, Dehghani TA Engineering (General). Civil engineering (General) 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 text en http://ir.unimas.my/id/eprint/44860/2/Implementing%20generative.pdf Ji, Hong Kang and Majid, Mirzaei and Lai, Sai Hin and Adnan, Dehghani and Amin, Dehghani (2024) Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation. Environmental Modelling & Software, 172. pp. 1-14. ISSN 1364-8152 https://www.sciencedirect.com/science/article/pii/S1364815223002827 https://doi.org/10.1016/j.envsoft.2023.105896
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Ji, Hong Kang
Majid, Mirzaei
Lai, Sai Hin
Adnan, Dehghani
Amin, Dehghani
Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation
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.
format Article
author Ji, Hong Kang
Majid, Mirzaei
Lai, Sai Hin
Adnan, Dehghani
Amin, Dehghani
author_facet Ji, Hong Kang
Majid, Mirzaei
Lai, Sai Hin
Adnan, Dehghani
Amin, Dehghani
author_sort Ji, Hong Kang
title 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_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_sort implementing generative adversarial network (gan) as a data-driven multi-site stochastic weather generator for flood frequency estimation
publisher Elsevier Ltd.
publishDate 2024
url 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
_version_ 1800728210906808320
score 13.244745

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