Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

• Published in: Sustainability 2023-06, Vol.15 (12), p.9687
• Main Authors: Derdour, Abdessamed, Abdo, Hazem Ghassan, Almohamad, Hussein, Alodah, Abdullah, Al Dughairi, Ahmed Abdullah, Ghoneim, Sherif S. M, Ali, Enas
• Format: Article
• Language: English
• Subjects: Agricultural production; Algorithms; Analysis; Aquifers; Arid environments; Arid zones; Artificial intelligence; Bicarbonates; Classification; Data collection; Decision making; Drinking water; Electrical conductivity; Electrical resistivity; Groundwater; Groundwater quality; Irrigation; Irrigation water; Laboratories; Machine learning; Management; Methods; Multiple regression models; Neural networks; Predictions; Quality assessment; Quality management; Quality standards; Rain; Resource management; Sodium; Support vector machines; Sustainability; Sustainability management; Water; Water management; Water quality; Water quality assessments; Water resources management; Water sampling; Water, Underground
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su15129687

Assessing water quality is crucial for improving global water resource management, particularly in arid regions. This study aims to assess and monitor the status of groundwater quality based on hydrochemical parameters and by using artificial intelligence (AI) approaches. The irrigation water quality index (IWQI) is predicted by using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers in Matlab’s classification learner toolbox. The classifiers are fed with the following hydrochemical input parameters: sodium adsorption ratio (SAR), electrical conductivity (EC), bicarbonate level (HCO3), chloride concentration (Cl), and sodium concentration (Na). The proposed methods were used to assess the quality of groundwater extracted from the desertic region of Adrar in Algeria. The collected groundwater samples showed that 9.64% of samples were of very good quality, 12.05% were of good quality, 21.08% were satisfactory, and 57.23% were considered unsuitable for irrigation. The IWQI prediction accuracies of the classifiers with the standardized, normalized, and raw data were 100%, 100%, and 90%, respectively. The cubic SVM with the normalized data develops the highest prediction accuracy for training and testing samples (94.2% and 100%, respectively). The findings of this work showed that the multiple regression model and machine learning could effectively assess water quality in desert zones for sustainable water management.