Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River Basin
This paper aims to evaluate two machine learning (ML) algorithms, namely, convolutional neural network (CNN) and long short-term memories (LSTM) deep learning algorithms, to predict the hydrological regime of the 3S River Basin under various climate change scenarios. Climate models CMCC-CMS, HadGEM-...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2023-08-01
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| Series: | Journal of Water and Climate Change |
| Subjects: | |
| Online Access: | http://jwcc.iwaponline.com/content/14/8/2902 |
| _version_ | 1797202768486727680 |
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| author | Quyen Nguyen Sangam Shrestha Suwas Ghimire S. Mohana Sundaram Wenchao Xue Salvatore G. P. Virdis Manisha Maharjan |
| author_facet | Quyen Nguyen Sangam Shrestha Suwas Ghimire S. Mohana Sundaram Wenchao Xue Salvatore G. P. Virdis Manisha Maharjan |
| author_sort | Quyen Nguyen |
| collection | DOAJ |
| description | This paper aims to evaluate two machine learning (ML) algorithms, namely, convolutional neural network (CNN) and long short-term memories (LSTM) deep learning algorithms, to predict the hydrological regime of the 3S River Basin under various climate change scenarios. Climate models CMCC-CMS, HadGEM-AO2, and MIROC5 were used to predict future climate and streamflow for three future periods: near-future (2020–2050), mid-future (2050–2080), and far-future (2080–2100) under two Representative Concentration Pathways (RCPs) 4.5 and 8.5. The future projection shows an increase in mean annual temperature from 0.08 to 4.3 °C by CMCC-CMS, from 0.13 to 4.4 °C by HadGEM-AO2, and −0.07 to 4.2 °C MIROC5 models. Similarly, the annual precipitation is projected to fluctuate from 13.3 to 62.5% by CMCC-CMS, from −12.4 to 26.1% by HadGEM-AO2, and from 6.9 to 49% by the MIROC5 model. The 3S River Basin expects an increasing trend in streamflow in the Srepok and Sesan Rivers, while the Sekong is projected to have reduced streamflow. ML models predicted the increasing flood risk in the Sekong and Sesan catchments with the increase of the Q5 index in the future but a decrease in the Srepok.
HIGHLIGHTS
Machine learning (ML) models were used to predict the hydrological changes in the 3S River Basin.;
The 3S River Basin is expected to be warmer and have fluctuation in rainfall patterns in the future.;
The Srepok and Sesan Rivers are expected to have an increasing trend of streamflow while the Sekong is projected to have reduced streamflow.;
ML models predicted the increasing flood risk in the Sekong and Sesan catchments.; |
| first_indexed | 2024-03-12T01:45:45Z |
| format | Article |
| id | doaj.art-c82726ffd4e84673b4d908a3ec423988 |
| institution | Directory Open Access Journal |
| issn | 2040-2244 2408-9354 |
| language | English |
| last_indexed | 2024-04-24T08:08:41Z |
| publishDate | 2023-08-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Water and Climate Change |
| spelling | doaj.art-c82726ffd4e84673b4d908a3ec4239882024-04-17T08:31:00ZengIWA PublishingJournal of Water and Climate Change2040-22442408-93542023-08-011482902291810.2166/wcc.2023.313313Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River BasinQuyen Nguyen0Sangam Shrestha1Suwas Ghimire2S. Mohana Sundaram3Wenchao Xue4Salvatore G. P. Virdis5Manisha Maharjan6 Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathum Thani 12120, Thailand Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathum Thani 12120, Thailand Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathum Thani 12120, Thailand Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathum Thani 12120, Thailand Environmental Engineering Management, School of Environment, Resources and Development, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathum Thani 12120, Thailand Remote Sensing and Geographic Information System, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathum Thani 12120, Thailand Center of Research for Environment, Energy and Water, Baluwatar, Kathmandu, Nepal This paper aims to evaluate two machine learning (ML) algorithms, namely, convolutional neural network (CNN) and long short-term memories (LSTM) deep learning algorithms, to predict the hydrological regime of the 3S River Basin under various climate change scenarios. Climate models CMCC-CMS, HadGEM-AO2, and MIROC5 were used to predict future climate and streamflow for three future periods: near-future (2020–2050), mid-future (2050–2080), and far-future (2080–2100) under two Representative Concentration Pathways (RCPs) 4.5 and 8.5. The future projection shows an increase in mean annual temperature from 0.08 to 4.3 °C by CMCC-CMS, from 0.13 to 4.4 °C by HadGEM-AO2, and −0.07 to 4.2 °C MIROC5 models. Similarly, the annual precipitation is projected to fluctuate from 13.3 to 62.5% by CMCC-CMS, from −12.4 to 26.1% by HadGEM-AO2, and from 6.9 to 49% by the MIROC5 model. The 3S River Basin expects an increasing trend in streamflow in the Srepok and Sesan Rivers, while the Sekong is projected to have reduced streamflow. ML models predicted the increasing flood risk in the Sekong and Sesan catchments with the increase of the Q5 index in the future but a decrease in the Srepok. HIGHLIGHTS Machine learning (ML) models were used to predict the hydrological changes in the 3S River Basin.; The 3S River Basin is expected to be warmer and have fluctuation in rainfall patterns in the future.; The Srepok and Sesan Rivers are expected to have an increasing trend of streamflow while the Sekong is projected to have reduced streamflow.; ML models predicted the increasing flood risk in the Sekong and Sesan catchments.;http://jwcc.iwaponline.com/content/14/8/2902climate changecnnhydrological modelinglstmmachine learningstreamflow |
| spellingShingle | Quyen Nguyen Sangam Shrestha Suwas Ghimire S. Mohana Sundaram Wenchao Xue Salvatore G. P. Virdis Manisha Maharjan Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River Basin Journal of Water and Climate Change climate change cnn hydrological modeling lstm machine learning streamflow |
| title | Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River Basin |
| title_full | Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River Basin |
| title_fullStr | Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River Basin |
| title_full_unstemmed | Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River Basin |
| title_short | Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River Basin |
| title_sort | application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3s river basin |
| topic | climate change cnn hydrological modeling lstm machine learning streamflow |
| url | http://jwcc.iwaponline.com/content/14/8/2902 |
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