Smart Water Management: Using Machine Learning to Analyze Water Quality Index

The availability of clean water is essential for public health and various industrial processes. However, sustaining the water quality is a challenge, because of the ever-increasing demand for water and the effect of human activities on water resources. Because of this, water quality prediction using the machine learning techniques is an emerging field of research that helps in forecasting water quality accurately. In this paper, a machine learning-based technique is proposed to predict quality of the water, using historical water quality data. Based on water quality index, usage of the water is being classified into three distinct categories, namely, drinking, agriculture and industrial. Then, performance of the model is evaluated for optimization using linear regression model, random forest, decision tree model and XG boost regression to enhance the accuracy of the model. The quality of the water can be determined based on the water quality parameters such as pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), nitrate, coliform and temperature. It helps in monitoring and managing the quality of water in real time, identifying potential risks and developing strategies to mitigate them. This experimental result proves that the proposed approach achieves high accuracy in predicting water quality and can be used as a reliable tool for water quality management. Water quality prediction using machine learning has the potential to improve the accuracy and efficiency of water resource management, leading to better environmental and public health outcomes.