Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

Abdessamed Derdour, Hazem Ghassan Abdo, Hussein Almohamad, Abdullah Alodah (), Ahmed Abdullah Al Dughairi, Sherif S. M. Ghoneim and Enas Ali
Additional contact information
Abdessamed Derdour: Artificial Intelligence Laboratory for Mechanical and Civil Structures and Soil, University Center of Naama, P.O. Box 66, Naama 45000, Algeria
Hazem Ghassan Abdo: Geography Department, Faculty of Arts and Humanities, Tartous University, Tartous P.O. Box 2147, Syria
Hussein Almohamad: Department of Geography, College of Arabic Language and Social Studies, Qassim University, Buraydah 51452, Saudi Arabia
Abdullah Alodah: Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia
Ahmed Abdullah Al Dughairi: Department of Geography, College of Arabic Language and Social Studies, Qassim University, Buraydah 51452, Saudi Arabia
Sherif S. M. Ghoneim: Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Enas Ali: Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11835, Egypt

Sustainability, 2023, vol. 15, issue 12, 1-20

Abstract: 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 (HCO 3 ), 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.

Keywords: ground water; water quality; IWQI; artificial intelligence; support vector machine; k-nearest neighbors; environment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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