Groundwater quality analysis and water quality index prediction by means ‎of machine learning methods‎ - Vamsi, B.; Chandra Sekhar, B.; Vakdevi , V.; Rohit, M.; Ibrahim, Md.; Uday Kiran, P.; Srinivas, Ch.; Gopi Kiran, M. (SPC Journal of Environmental Sciences, 2025)
The study of variations in the quality of groundwater due to subsequent changes in society is a ‎matter of unease as groundwater is regarded as a vital water supply source among all the water ‎sources available. Water quality assessment via monitoring paves the source for arbitrating the ‎appropriateness of quality of water for various purposes including management of water quality. ‎This study was mainly focused on monitoring and assessing the quality of groundwater collected ‎from Madalvarigudem and Mudirajupalem, both from Krishna district, Andhra Pradesh, India. ‎Standard methods were used for analyzing the collected samples for five parameters that include ‎alkalinity, acidity, pH, total hardness (TH) and total dissolved solids (TDS). The groundwater ‎quality parameter analysis indicate high values of pH and TDS, especially in the Mudirajupalem ‎samples as it is located near to agricultural fields. Further, the water quality index (WQI) values ‎supported that groundwater collected from Mudirajupalem is unfit for drinking purpose. This ‎study employed random forest, decision tree and gradient boosted tree techniques for predicting ‎the WQI values and, a hierarchical reconciliation algorithm proved favorable in foretelling water ‎quality parameters. The predicted WQI values meticulously complemented with the obtained ‎experimental results, further endorsing that Mudirajupalem groundwater is not apt for public ‎consumption. Further, this study proposes implementing simple rainwater harvesting systems, which ‎would eventually result in improving the groundwater recharge and maintain groundwater balance ‎so as to making it as a viable source of life‎.