Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya River
Frequent saltwater intrusions in the Chao Phraya River have had an impact on water supply to the residents of Bangkok and nearby areas. Although relocation of the raw water station is a long-term solution, it requires a large amount of time and investment. At present, knowing in advance when an intr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/5/741 |
| _version_ | 1797473447014563840 |
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| author | Jiramate Changklom Phakawat Lamchuan Adichai Pornprommin |
| author_facet | Jiramate Changklom Phakawat Lamchuan Adichai Pornprommin |
| author_sort | Jiramate Changklom |
| collection | DOAJ |
| description | Frequent saltwater intrusions in the Chao Phraya River have had an impact on water supply to the residents of Bangkok and nearby areas. Although relocation of the raw water station is a long-term solution, it requires a large amount of time and investment. At present, knowing in advance when an intrusion occurs will support the waterworks authority in their operations. Here, we propose a method to forecast the salinity at the raw water pumping station from 24 h up to 120 h in advance. Each of the predictor variables has a physical impact on salinity. We explore a number of model candidates based on two common fitting methods: multiple linear regression and the artificial neural network. During model development, we found that the model behaved differently when the water level was high than when the water level was low (water level is measured at a point 164 km upstream of the raw water pumping station); therefore, we propose a novel multilevel model approach that combines different sub-models, each of which is suitable for a particular water level. The models have been trained and selected through cross-validation, and tested on real data. According to the test results, the salinity can be forecasted with an RMSE of 0.054 g L\({^{-1}}\) at a forecast period of 24 h and up to 0.107 g L\({^{-1}}\) at a forecast period of 120 h. |
| first_indexed | 2024-03-09T20:15:32Z |
| format | Article |
| id | doaj.art-72dfa7cb541e4eb88af5bd3a75e188df |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T20:15:32Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-72dfa7cb541e4eb88af5bd3a75e188df2023-11-24T00:02:28ZengMDPI AGWater2073-44412022-02-0114574110.3390/w14050741Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya RiverJiramate Changklom0Phakawat Lamchuan1Adichai Pornprommin2Department of Water Resources Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, ThailandDepartment of Water Resources Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, ThailandDepartment of Water Resources Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, ThailandFrequent saltwater intrusions in the Chao Phraya River have had an impact on water supply to the residents of Bangkok and nearby areas. Although relocation of the raw water station is a long-term solution, it requires a large amount of time and investment. At present, knowing in advance when an intrusion occurs will support the waterworks authority in their operations. Here, we propose a method to forecast the salinity at the raw water pumping station from 24 h up to 120 h in advance. Each of the predictor variables has a physical impact on salinity. We explore a number of model candidates based on two common fitting methods: multiple linear regression and the artificial neural network. During model development, we found that the model behaved differently when the water level was high than when the water level was low (water level is measured at a point 164 km upstream of the raw water pumping station); therefore, we propose a novel multilevel model approach that combines different sub-models, each of which is suitable for a particular water level. The models have been trained and selected through cross-validation, and tested on real data. According to the test results, the salinity can be forecasted with an RMSE of 0.054 g L\({^{-1}}\) at a forecast period of 24 h and up to 0.107 g L\({^{-1}}\) at a forecast period of 120 h.https://www.mdpi.com/2073-4441/14/5/741salinity forecastdata-driven approachesthe Chao Phraya River |
| spellingShingle | Jiramate Changklom Phakawat Lamchuan Adichai Pornprommin Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya River Water salinity forecast data-driven approaches the Chao Phraya River |
| title | Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya River |
| title_full | Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya River |
| title_fullStr | Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya River |
| title_full_unstemmed | Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya River |
| title_short | Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya River |
| title_sort | salinity forecasting on raw water for water supply in the chao phraya river |
| topic | salinity forecast data-driven approaches the Chao Phraya River |
| url | https://www.mdpi.com/2073-4441/14/5/741 |
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