Prediction of Different Water Quality Parameters by Machine Learning Technique

This paper analyses water pollution data focusing on Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), pH, and turbidity. Each parameter has specific acceptable ranges to ensure the water is safe and suitable for consumption. BOD, COD, pH and turbidity levels using a Comma separated value (CSV) dataset. Initially, the data is pre-processed by cleaning it to handle missing values and outliers, ensuring consistency and accuracy. The dataset was then divided into training and testing subsets. For modelling, K-Nearest Neighbor(KNN) algorithm of machine learning will be employed. The model was trained on the processed data and evaluated using metrics such as the mean squared error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) to assess pollutants label of BOD, COD, pH and turbidity of Damodar at Asansol, Raniganj, Andal and Durgapur with some predefined threshold values based on past experience. Finally, the above analysis will be interpreted to understand the patterns and factors influencing pollution levels of BOD, COD, pH and turbidity and the results are found to be in very good agreement.