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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Autores principales:	Dang An Tran, Maki Tsujimura, Nam Thang Ha, Van Tam Nguyen, Doan Van Binh, Thanh Duc Dang, Quang-Van Doan, Dieu Tien Bui, Trieu Anh Ngoc, Le Vo Phu, Pham Thi Bich Thuc, Tien Dat Pham
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	CatBoost Regression Influencing factors Groundwater salinization Multi-layer coastal aquifers Mekong Delta Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/65ec6764a24446f6949d5852315ad03f
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spelling	oai:doaj.org-article:65ec6764a24446f6949d5852315ad03f2021-12-01T04:53:42ZEvaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam1470-160X10.1016/j.ecolind.2021.107790https://doaj.org/article/65ec6764a24446f6949d5852315ad03f2021-08-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21004556https://doaj.org/toc/1470-160XGroundwater 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 its influencing factors. In this study, we evaluate state-of-the-art machine learning (ML) algorithms for predicting groundwater salinity and identify its influencing factors. We conducted a study for the coastal multi-layer aquifers of the Mekong River Delta (Vietnam), using a geodatabase of 216 groundwater samples and 14 conditioning factors. We compared the predictive performances of different ML techniques, i.e., the Random Forest Regression (RFR), the Extreme Gradient Boosting Regression (XGBR), the CatBoost Regression (CBR), and the Light Gradient Boosting Regression (LGBR) models. The model performance was assessed by using root-mean-square error (RMSE), coefficient of determination (R2), the Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results show that the CBR model has the highest performance with both training (R2 = 0.999, RMSE = 29.90) and testing datasets (R2 = 0.84, RMSE = 205.96, AIC = 720.60, and BIC = 751.04). Ten of the 14 influencing factors, including the distance to saline sources, the depth of screen well, the groundwater level, the vertical hydraulic conductivity, the operation time, the well density, the extraction capacity, the thickness of the aquitard, the distance to fault, and the horizontal hydraulic conductivity are the most important factors for groundwater salinity prediction. The results provide insights for policymakers in proposing remediation and management strategies for groundwater salinity issues in the context of excessive groundwater exploitation in coastal lowland regions. Since the human-induced influencing factors have significantly influenced groundwater salinization, urgent actions should be taken into consideration to ensure sustainable groundwater management in the coastal areas of the Mekong River Delta.Dang An TranMaki TsujimuraNam Thang HaVan Tam NguyenDoan Van BinhThanh Duc DangQuang-Van DoanDieu Tien BuiTrieu Anh NgocLe Vo PhuPham Thi Bich ThucTien Dat PhamElsevierarticleCatBoost RegressionInfluencing factorsGroundwater salinizationMulti-layer coastal aquifersMekong DeltaEcologyQH540-549.5ENEcological Indicators, Vol 127, Iss , Pp 107790- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CatBoost Regression Influencing factors Groundwater salinization Multi-layer coastal aquifers Mekong Delta Ecology QH540-549.5
spellingShingle	CatBoost Regression Influencing factors Groundwater salinization Multi-layer coastal aquifers Mekong Delta Ecology QH540-549.5 Dang An Tran Maki Tsujimura Nam Thang Ha Van Tam Nguyen Doan Van Binh Thanh Duc Dang Quang-Van Doan Dieu Tien Bui Trieu Anh Ngoc Le Vo Phu Pham Thi Bich Thuc Tien Dat Pham Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam
description	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 its influencing factors. In this study, we evaluate state-of-the-art machine learning (ML) algorithms for predicting groundwater salinity and identify its influencing factors. We conducted a study for the coastal multi-layer aquifers of the Mekong River Delta (Vietnam), using a geodatabase of 216 groundwater samples and 14 conditioning factors. We compared the predictive performances of different ML techniques, i.e., the Random Forest Regression (RFR), the Extreme Gradient Boosting Regression (XGBR), the CatBoost Regression (CBR), and the Light Gradient Boosting Regression (LGBR) models. The model performance was assessed by using root-mean-square error (RMSE), coefficient of determination (R2), the Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The results show that the CBR model has the highest performance with both training (R2 = 0.999, RMSE = 29.90) and testing datasets (R2 = 0.84, RMSE = 205.96, AIC = 720.60, and BIC = 751.04). Ten of the 14 influencing factors, including the distance to saline sources, the depth of screen well, the groundwater level, the vertical hydraulic conductivity, the operation time, the well density, the extraction capacity, the thickness of the aquitard, the distance to fault, and the horizontal hydraulic conductivity are the most important factors for groundwater salinity prediction. The results provide insights for policymakers in proposing remediation and management strategies for groundwater salinity issues in the context of excessive groundwater exploitation in coastal lowland regions. Since the human-induced influencing factors have significantly influenced groundwater salinization, urgent actions should be taken into consideration to ensure sustainable groundwater management in the coastal areas of the Mekong River Delta.
format	article
author	Dang An Tran Maki Tsujimura Nam Thang Ha Van Tam Nguyen Doan Van Binh Thanh Duc Dang Quang-Van Doan Dieu Tien Bui Trieu Anh Ngoc Le Vo Phu Pham Thi Bich Thuc Tien Dat Pham
author_facet	Dang An Tran Maki Tsujimura Nam Thang Ha Van Tam Nguyen Doan Van Binh Thanh Duc Dang Quang-Van Doan Dieu Tien Bui Trieu Anh Ngoc Le Vo Phu Pham Thi Bich Thuc Tien Dat Pham
author_sort	Dang An Tran
title	Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam
title_short	Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam
title_full	Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam
title_fullStr	Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam
title_full_unstemmed	Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam
title_sort	evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the mekong delta, vietnam
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/65ec6764a24446f6949d5852315ad03f
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Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta, Vietnam

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