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Open AccessArticle

Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

1
Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Reclamation of Arid and Mountainous Regions Department, Faculty of Natural Resources, University of Tehran, Karaj 31585-77871, Iran
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Soil Conservation and Watershed Management Research Department, West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia 57169-63963, Iran
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Technical Expert at UNDP/DOE Conservation of Iranian Wetlands Project, Tehran 14639-14111, Iran
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Deputy for Marine Environment and Wetlands, Iran Department of Environment, Tehran 73831-4155, Iran
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
*
Author to whom correspondence should be addressed.
Water 2020, 12(10), 2770; https://doi.org/10.3390/w12102770
Received: 15 August 2020 / Revised: 27 September 2020 / Accepted: 30 September 2020 / Published: 5 October 2020
(This article belongs to the Section Hydrology and Hydrogeology)
Groundwater resources, unlike surface water, are more vulnerable to disturbances and contaminations, as they take a very long time and significant cost to recover. So, predictive modeling and prevention strategies can empower policymakers for efficient groundwater governance through informed decisions and recommendations. Due to the importance of groundwater quality modeling, the hardness susceptibility mapping using machine learning (ML) models has not been explored. For the first time, the current research aimed to predict groundwater hardness susceptibility using the ML models. The performance of two ensemble models of boosted regression trees (BRT) and random forest (RF) is investigated through the arrangement of a comparative study with multivariate discriminant analysis (MDA). According to the hardness values in 135 groundwater quality monitoring wells, the hard and soft water are determined; then, 11 predictor variables including distance from the sea (DFS), land use, elevation, distance from the river (DFR), depth to groundwater (DTGW), pH, precipitation (PCP), evaporation (E), groundwater level (GWL), curvature, and lithology are used for predicting the groundwater hardness susceptibility map. Results indicated that the variables of DFR, DTGW, elevation, and DFS had a higher contribution to the modeling process. So, the high harness areas are mostly related to low elevations, low DTGW, and proximity to river and sea, which facilitate the percolation conditions for minerals containing calcium or magnesium into groundwater. View Full-Text
Keywords: water quality assessment; groundwater; hardness; susceptibility; machine learning; boosted regression trees; hazard map; deep learning; geo-informatics; multivariate discriminant analysis; random forest; hydrological model; hydro-informatics; big data; natural hazard water quality assessment; groundwater; hardness; susceptibility; machine learning; boosted regression trees; hazard map; deep learning; geo-informatics; multivariate discriminant analysis; random forest; hydrological model; hydro-informatics; big data; natural hazard
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Mosavi, A.; Hosseini, F.S.; Choubin, B.; Abdolshahnejad, M.; Gharechaee, H.; Lahijanzadeh, A.; Dineva, A.A. Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models. Water 2020, 12, 2770.

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