Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

被引:71
作者
Mosavi, Amirhosein [1 ,2 ]
Hosseini, Farzaneh Sajedi [3 ]
Choubin, Bahram [4 ]
Abdolshahnejad, Mahsa [3 ]
Gharechaee, Hamidreza [3 ,5 ]
Lahijanzadeh, Ahmadreza [6 ]
Dineva, Adrienn A. [7 ]
机构
[1] Ton Duc Thang Univ, Environm Qual Atmospher Sci & Climate Change Res, Ho Chi Minh City, Vietnam
[2] Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam
[3] Univ Tehran, Fac Nat Resources, Reclamat Arid & Mountainous Reg Dept, Karaj 3158577871, Iran
[4] AREEO, West Azarbaijan Agr & Nat Resources Res & Educ Ct, Soil Conservat & Watershed Management Res Dept, Orumiyeh 5716963963, Iran
[5] UNDP DOE Conservat Iranian Wetlands Project, Tehran 1463914111, Iran
[6] Iran Dept Environm, Deputy Marine Environm & Wetlands, Tehran 738314155, Iran
[7] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
关键词
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; QUALITY ASSESSMENT; RANDOM FOREST; VULNERABILITY; MULTIVARIATE; AQUIFER; RISK; GIS; CATCHMENT; BIVARIATE; BASIN;
D O I
10.3390/w12102770
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
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页数:17
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