Academic Journal
Salinity Intrusion Prediction in the Estuary Using Machine Learning: Vietnam's Mekong Delta Tested for Global Study.
| Τίτλος: | Salinity Intrusion Prediction in the Estuary Using Machine Learning: Vietnam's Mekong Delta Tested for Global Study. |
|---|---|
| Συγγραφείς: | Nguyen, Huu Duy |
| Πηγή: | Iranian Journal of Science & Technology: Transactions of Civil Engineering; Aug2026, Vol. 50 Issue 4, p4305-4323, 19p |
| Θεματικοί όροι: | Water salinization, Machine learning, Ensemble learning, Estuary management, Long short-term memory, Recurrent neural networks, Saltwater encroachment |
| Γεωγραφικοί όροι: | Vietnam, Mekong River |
| Περίληψη: | The estuary is an important ecological system that faces significant challenges from salinity intrusion, which has had significant effects on human activities. Accurate prediction of water salinity plays an important role in the contribution of irrigation water to agricultural development, especially in the context of climate change and the rise of sea level. The objective of this study is the application of machine learning, namely long- and short-term memory (LSTM), gated recurrent units (GRUs), and hybrid machine learning (LSTM-GRU and LSTM-GRU- Sailfish optimiser algorithm (SFO)) to predict water salinity in the Hau River estuary, in Mekong detail in Vietnam. More specifically, (i) this study develops a hybrid model by combining LSTM, GRU, and LSTM-GRU networks, optimised by the Sailfish Optimizer (SFO) algorithm. (ii) this study compares the proposed models to determine the best model to predict soil salinity in the study area; (iii) this study analyses the trend of salinity change to support decision makers or local authority for the optimisation of water resource management. In this study, the water level and the discharge from the river were used to predict the water and 10 days were selected to predict one, five, seven and ten days ahead. seven, The results showed that the LSTM-GRU-SFO model outperformed other models in terms of accuracy. Specifically, this model achieved the best performance with the value of R² = 0.894, RMSE = 3.18, MAE = 1.37 for the prediction and R² = 0.880, RMSE = 3.11, MAE = 1.34 for the prediction of five days, R2 = 0.854, RMSE = 3.46, MAE = 1.47 for the prediction of seven days and R² = 0.815, RMSE = 3.87, MAE = 1.42 for the prediction of ten days. This accuracy presents that the LSTM-GRU-SFO model effectively captures not only temporal salinity but also high reliability over all prediction horizons. The results of this study justify the effectiveness of machine learning to predict water salinity in estuaries. The knowledge obtained in this study can provide important support for water irrigation intelligence management strategies not only in Vietnam but also in other regions of the world. [ABSTRACT FROM AUTHOR] |
| Copyright of Iranian Journal of Science & Technology: Transactions of Civil Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s40996-025-02034-7 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 196028459 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.42456054688 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Salinity Intrusion Prediction in the Estuary Using Machine Learning: Vietnam's Mekong Delta Tested for Global Study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nguyen%2C+Huu+Duy%22">Nguyen, Huu Duy</searchLink> – Name: TitleSource Label: Source Group: Src Data: Iranian Journal of Science & Technology: Transactions of Civil Engineering; Aug2026, Vol. 50 Issue 4, p4305-4323, 19p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Water+salinization%22">Water salinization</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Estuary+management%22">Estuary management</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Saltwater+encroachment%22">Saltwater encroachment</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Vietnam%22">Vietnam</searchLink><br /><searchLink fieldCode="DE" term="%22Mekong+River%22">Mekong River</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The estuary is an important ecological system that faces significant challenges from salinity intrusion, which has had significant effects on human activities. Accurate prediction of water salinity plays an important role in the contribution of irrigation water to agricultural development, especially in the context of climate change and the rise of sea level. The objective of this study is the application of machine learning, namely long- and short-term memory (LSTM), gated recurrent units (GRUs), and hybrid machine learning (LSTM-GRU and LSTM-GRU- Sailfish optimiser algorithm (SFO)) to predict water salinity in the Hau River estuary, in Mekong detail in Vietnam. More specifically, (i) this study develops a hybrid model by combining LSTM, GRU, and LSTM-GRU networks, optimised by the Sailfish Optimizer (SFO) algorithm. (ii) this study compares the proposed models to determine the best model to predict soil salinity in the study area; (iii) this study analyses the trend of salinity change to support decision makers or local authority for the optimisation of water resource management. In this study, the water level and the discharge from the river were used to predict the water and 10 days were selected to predict one, five, seven and ten days ahead. seven, The results showed that the LSTM-GRU-SFO model outperformed other models in terms of accuracy. Specifically, this model achieved the best performance with the value of R² = 0.894, RMSE = 3.18, MAE = 1.37 for the prediction and R² = 0.880, RMSE = 3.11, MAE = 1.34 for the prediction of five days, R<superscript>2</superscript> = 0.854, RMSE = 3.46, MAE = 1.47 for the prediction of seven days and R² = 0.815, RMSE = 3.87, MAE = 1.42 for the prediction of ten days. This accuracy presents that the LSTM-GRU-SFO model effectively captures not only temporal salinity but also high reliability over all prediction horizons. The results of this study justify the effectiveness of machine learning to predict water salinity in estuaries. The knowledge obtained in this study can provide important support for water irrigation intelligence management strategies not only in Vietnam but also in other regions of the world. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Iranian Journal of Science & Technology: Transactions of Civil Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=196028459 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s40996-025-02034-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 4305 Subjects: – SubjectFull: Vietnam Type: general – SubjectFull: Mekong River Type: general – SubjectFull: Water salinization Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Estuary management Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Recurrent neural networks Type: general – SubjectFull: Saltwater encroachment Type: general Titles: – TitleFull: Salinity Intrusion Prediction in the Estuary Using Machine Learning: Vietnam's Mekong Delta Tested for Global Study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nguyen, Huu Duy IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22286160 Numbering: – Type: volume Value: 50 – Type: issue Value: 4 Titles: – TitleFull: Iranian Journal of Science & Technology: Transactions of Civil Engineering Type: main |
| ResultId | 1 |