Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

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