Application of Machine Learning Model for Assessing Water Quality Index

Predicting water quality is essential for safeguarding public health, protect the environment, ensuring sustainable resource management, and meet regulatory requirements. The complexity of forecasting water quality across a catchment area arises from the inherent non-linear interactions among water quality parameters and their temporal and spatial variability. This chapter introduces various machine learning models to improve water quality. In this chapter, the author delves into the foundational concepts of machine learning models and introduce an approach based on 8 Machine Learning Models for assessing Water Quality Index (WQI). The predictive performance of the ML models was assessed using different metrics such as accuracy, classification error, recall, precision, specificity, balanced accuracy and F1 score. The model considers physical and chemical parameters such as pH, EC, turbidity, total hardness, Total alkalinity, TDS, Mg, Ca, Cl−, F−, \({\text{NO}}_{3}^{ - }\) and \({\text{HCO}}_{3}^{ - }\) to predict the water quality index (WQI). As a real-world application, a case study of Farrukhabad district in Uttar Pradesh, where ML models were successfully used to predict groundwater quality. Given precise predictions of water quality, the findings could contribute to enhancing the National Environmental Policy on water resources through ongoing improvements in water quality.