Machine Learning For Groundwater Quality Classification: A Step Towards Economic and Sustainable Groundwater Quality Assessment Process

Aymen Zegaar (), Samira Ounoki () and Abdelmoutia Telli ()
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Aymen Zegaar: Research Laboratory in Subterranean and Surface Hydraulics, Mohamed Khider University
Samira Ounoki: Research Laboratory in Subterranean and Surface Hydraulics, Mohamed Khider University
Abdelmoutia Telli: Mohamed Khider University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 2, No 12, 637 pages

Abstract: Abstract Evaluation of water quality is essential for protecting both the environment and human wellbeing. There is a paucity of research on using machine learning for classification of groundwater used for irrigation with fewer input parameters and still getting satisfactory results, despite earlier studies exploring its application in evaluating water quality. Studies are required to determine the feasibility of using machine learning to classify groundwater used for irrigation using minimal input parameters. In this study, we developed machine learning models to simulate the Irrigation Water Quality Index (IWQI) and an economic model that used an optimal number of inputs with the highest possible accuracy. We utilized eight classification algorithms, including the LightGBM classifier, CatBoost, Extra Trees, Random Forest, Gradient Boosting classifiers, Support Vector Machines, Multi-Layer Perceptrons, and K-Nearest Neighbors Algorithm. Two scenarios were considered, the first using six inputs, including conductivity, chloride ( $$\mathrm{Cl}^{-}$$ Cl - ), bicarbonate ( $$\mathrm{HCO}_3{}^{-}$$ HCO 3 - ), sodium ( $$\mathrm{Na}^{+}$$ Na + ), calcium ( $$\mathrm{Ca}^{2+}$$ Ca 2 + ), and magnesium ( $$\mathrm{Mg}^{2+}$$ Mg 2 + ), and the second using three parameters, including total hardness (TH), chloride ( $$\mathrm{Cl}^{-}$$ Cl - ), and sulfate ( $${\mathrm{SO}_4{}^{2-}}$$ SO 4 2 - ) that were selected based on the Mutual Information (MI) result. The models achieved satisfactory performance, with the LightGBM classifier as the best model, yielding a 91.08% F1 score using six inputs, and the Extra Trees classifier as the best model, yielding an 86.30% F1 score using three parameters. Our findings provide a valuable contribution to the development of accurate and efficient machine learning models for water quality evaluation.

Keywords: Groundwater quality classification; Machine learning; Ensemble algorithms; MLP; Economic model; Mutual information (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-023-03690-y

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