Affiliations: Sri Guru Tegh Bahadur Khalsa College, University of Delhi, New Delhi, India | Ramjas College, University of Delhi, New Delhi, India | Bharati Vidyapeeth’s College of Engineering, New Delhi, India | Department of Computer Science & Engineering, Graphic Era (Deemed to be University), Dehradun, India
Abstract: Water quality assessment is essential for safeguarding public health and protecting water resources. This study focused on predicting water quality, specifically the presence of total coliforms, using various machine-learning techniques. The present study utilises a publicly available dataset encompassing the geographical area of India consisting of various physical water quality parameters. Various regression techniques were applied to the dataset after appropriate pre-processing including feature selection and normalisation. The findings demonstrate that gradient boosting regression outperforms other methods, achieving high accuracy with mean absolute error (MAE) of 0.0349, mean squared error (MSE) of 0.0038, and root mean squared error (RMSE) of 0.0620. Conductivity and temperature emerged as the most influential factors in total coliform prediction, as revealed by feature importance analysis. These results contribute to water quality understanding, aiding water resource management for public health protection. By accurately predicting total coliform presence, proactive measures can be taken timely to mitigate and minimise health risks associated with microbial contamination.
Keywords: Water quality index, conductivity, temperature, machine learning, total coliform, regression
