Prediction and classification of groundwater quality index (WQI) using regression learning models

Groundwater has developed into a vital natural resource as a result of its significant use in home applications, particularly for drinking, as well as in the agricultural and industrial sectors. The quality and quantity of groundwater have varied greatly across time and space. Water Quality Index (WQI) which depends on many parameters, remains a critical indicator of the quality of water, which leads to effective water management. The administrators will be benefitted if an automatic system for predicting water quality exists. The primary aim of this study is to design a model to predict the groundwater quality in different districts of Tamil Nadu (TN), India, using Machine Learning (ML) techniques. The available data constitutes the physical and chemical characteristics of groundwater such as pH, electrical conductivity (EC), TH, Ca2+, Mg2+, Na+, HCO3-, NO3-, SO42-, and Cl- along with its suitability for irrigation and drinking purposes. In this study, many ML algorithms were implemented, and the results were compared.