Groundwater quality modeling and determining critical points: a comparison of machine learning to Best–Worst Method

• Published in: Environmental science and pollution research international Vol. 30; no. 54; pp. 115758 - 115775
• Main Authors: Nasiri Khiavi, Ali, Mostafazadeh, Raoof, Adhami, Maryam
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Acidity; Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Bicarbonates; calcium; Calcium ions; Chlorine; Critical point; decision support systems; Decision trees; Developing countries; Dissolved solids; Earth and Environmental Science; Ecotoxicology; Electrical conductivity; Electrical resistivity; Environment; Environmental Chemistry; Environmental Health; Groundwater; Groundwater quality; Iran; LDCs; Learning algorithms; Machine learning; Magnesium; Mathematical models; Modelling; Parameters; potassium; Programming languages; regression analysis; Research Article; Resource exploitation; Sodium; Statistical analysis; sulfates; Support vector machines; Total dissolved solids; Waste Water Technology; Water Management; Water Pollution Control; Water quality; Water resources; Iran; Groundwater pollution; Geospatial modeling; MCDM; Groundwater resources; Water quality
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-023-30530-8

In Iran, similar to other developing countries, groundwater quality has been seriously threatened. Therefore, this study aimed to apply Machine Learning Algorithms (MLAs) in Groundwater Quality Modeling (GQM) and determine the optimal algorithm using the Best–Worst Method (BWM) in Ardabil province, Iran. Groundwater quality parameters included calcium (Ca 2+ ), magnesium (Mg 2+ ), sodium (Na + ), potassium (K + ), chlorine (Cl − ), sulfate (SO 4 − ), total dissolved solids (TDS), bicarbonate (HCO 3 − ), electrical conductivity (EC), and acidity (pH). In the following, seven MLAs, including Support Vector Regression (SVR), Random Forest (RF), Decision Tree Regressor (DTR), K-Nearest Neighbor (KNN), Naïve Bayes, Simple Linear Regression (SLR), and Support Vector Machine (SVM), were used in the Python programming language, and groundwater quality was modeled. Finally, BWM was used to validate the results of MLAs. The results of examining the error statistics in determining the optimal algorithm in groundwater quality modeling showed that the RF algorithm with values of MAE = 0.28, MSE = 0.12, RMSE = 0.35, and AUC = 0.93 was selected as the most optimal MLA. The Schoeller diagram also showed that various ion ratios, including Na + K, Ca 2+ , Mg 2+ , Cl − , and HCO 3 +CO 3 , in most of the sampled points had upward average values. Based on the results of the BWM method, it can be concluded that a great similarity was observed between the results of the RF algorithm and the classification of the BWM method. These results showed that more than 50% of the studied area had low quality based on hydro-chemical parameters of groundwater quality. The findings of this research can assist managers and planners in developing suitable management models and implementing appropriate strategies for the optimal exploitation of groundwater resources.