Groundwater Quality Analysis and Drinkability Prediction using Artificial Intelligence

Water quality strongly influences sustainable growth of a healthy society and green environment. According to the International Initiative on Water Quality (IIWQ) of the UNESCO Intergovernmental Hydrological Programme (IHP), it is essential to address water-quality issues holistically in developed and developing countries. Due to rapid urbanization and industrialization in many developing countries, groundwater - one of the major sources of drinking is getting highly affected. The traditional laboratory-based chemical testing process with conventional statistical methods is often used to analyze water quality. However, it is time-consuming. Recently, Artificial Intelligence (AI) based approaches have proven to be a better alternative for analysis and prediction of the quality of water, provided with its chemical components’ data. In this paper, we present research focusing on groundwater quality analysis using Artificial Intelligence (AI) in a case study of Odisha, an eastern- state of India and the data acquired from the Northern delta, the North Central Coast of Vietnam. The dataset in Vietnam is collected by the Ministry of Natural Resources and Environment, providing technical regulations on water resources monitoring. The Central Groundwater Board and the Government of India collect the dataset from India. The target problem is formulated as a multi-class classification problem to predict groundwater quality for drinking suitability by WHO standards. AI methodologies such as logistic regression, K-NN, Support Vector Machine (SVM) variants, decision tree, AdaBoost and XGBoost are used. Prediction results have demonstrated that Adaboost, the XGBoost and the Polynomial SVM model accurately classified the Water Quality Classes with an accuracy of 92% and 98%, respectively. It would help decision-makers effectively choose the best source of water for drinking.