International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Predicting and Evaluating Water Quality using Machine Learning in Maharashtra, India

Author(s) Sanaya Kotwal
Country India
Abstract Water quality is critical for public health and environmental sustainability, necessitating effective monitoring to prevent contamination. In this study, we focus on predicting and evaluating water quality in Maharashtra, India, using machine learning techniques. Groundwater contamination in Maharashtra is a significant issue due to poor waste management, yet research in this area is limited. Traditional water quality monitoring methods involve complex calculations based on fixed parameters, which can lead to errors. This study aims to streamline the monitoring process by identifying the most significant features, thereby saving time, money, and energy. We calculated the Water Quality Index (WQI) using the Weighted Arithmetic Mean method, analyzing data from 2012 to 2022 from the National Water Monitoring Program in India. The analysis identified three key parameters, BOD, pH, and Fecal Coliform, as most correlated with the WQI. Machine learning techniques, including regression and classification, were employed to predict WQI and Water Quality Classification (WQC). The results indicate that Polynomial Regression and Ridge Regression achieved high accuracy in predicting the WQI, while the Decision Tree classifier excelled in WQC classification. This research demonstrates the potential of machine learning to enhance water quality monitoring, offering a cost-effective solution for managing water resources in Maharashtra.
Keywords Water Quality Evaluation, Maharashtra, India, Water Quality Prediction, Supervised Machine Learning
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-10-08
Cite This Predicting and Evaluating Water Quality using Machine Learning in Maharashtra, India - Sanaya Kotwal - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.28574
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.28574
Short DOI https://doi.org/g794z9
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