Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam

Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam

Groundwater salinization is considered as a major environmental problem in worldwide coastal areas, influencing ecosystems and human health. However, an accurate prediction of salinity concentration in groundwater remains a challenge due to the complexity of groundwater salinization processes and it...

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Bibliographic Details
Main Authors: Anh Ngoc, T. (Author), Binh, D.V (Author), Bui, D.T (Author), Dang, T.D (Author), Doan, Q.-V (Author), Ha, N.T (Author), Nguyen, V.T (Author), Pham, T.D (Author), Phu, L.V (Author), Thuc, P.T.B (Author), Tran, D.A (Author), Tsujimura, M. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2021
Subjects:
algorithm
Aquifers
Catboost regression
CatBoost Regression
coastal aquifer
Decision trees
detection method
Forecasting
Groundwater resources
Groundwater salinities
Groundwater salinization
Groundwater salinizations
hydraulic conductivity
Hydraulic conductivity
Hydrogeology
Influencing factor
Influencing factors
machine learning
Machine learning
Machine learning algorithms
Mean square error
Mekong Delta
Mekong River
Multi-layer coastal aquifer
Multi-layer coastal aquifers
Multi-layers
Regression analysis
Root mean square errors
salinity
Salt water intrusion
Viet Nam
Water management
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