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contributor authorVan-Hau Nguyen
contributor authorVan-Thang Duong
contributor authorAnh Le
contributor authorHa T. H. Mai
contributor authorHue Thi Dang
contributor authorDiep Phan
contributor authorJanak Adhikari
date accessioned2024-12-24T10:30:53Z
date available2024-12-24T10:30:53Z
date copyright10/1/2024 12:00:00 AM
date issued2024
identifier otherJHYEFF.HEENG-6223.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4299062
description abstractThe Mekong Delta is renowned as one of the world’s most productive regions for rice cultivation. However, it faces significant challenges due to salinity intrusion, where seawater from the South China Sea flows upstream into the delta area. Early warning systems that can assess the severity of salinity intrusion events are crucial in mitigating its negative impacts. In this study, various machine learning strategies are presented to forecast salinity intrusion in the Mekong Delta. The available data are fully utilized using the principal component analysis technique in conjunction with 13 advanced machine learning algorithms. The results demonstrate that logistic regression, support vector classification, and quadratic discriminant analysis models consistently achieve accuracies higher than 86% across most data sets. Additionally, random forest, extra trees, gradient boosting, and bagging classifier models demonstrate accuracies of 95% and 100% for specific data sets. These findings highlight the effectiveness of machine learning models in forecasting salinity intrusion and present a range of algorithms and data sets that can be employed for accurate predictions in the Mekong Delta region.
publisherAmerican Society of Civil Engineers
titleMachine Learning–Based Early-Warning Systems for Salinity Intrusion in the Mekong River Delta, Vietnam
typeJournal Article
journal volume29
journal issue5
journal titleJournal of Hydrologic Engineering
identifier doi10.1061/JHYEFF.HEENG-6223
journal fristpage04024032-1
journal lastpage04024032-11
page11
treeJournal of Hydrologic Engineering:;2024:;Volume ( 029 ):;issue: 005
contenttypeFulltext


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