Comparative Analysis of Physicochemical water parameters with reference to Environmental Quality Monitoring using machine learning techniques.

Registration ID: IJNRD_304683

Published ID: IJNRD2503238

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Jay chotaliya, Dr. Santosh Kumar Singh, Mrs. Amit Kumar Pandey

Keywords: Water quality assessment, Machine learning, Public health, Environmental management, Predictive modeling

— This study evaluates the efficacy of machine learning (ML) techniques in analyzing physicochemical water parameters for environmental quality monitoring. Leveraging datasets containing pH, dissolved oxygen (DO), turbidity, conductivity, biochemical oxygen demand (BOD), and nutrient concentrations, we trained and tested multiple ML models to predict water quality indices (WQI) and classify water bodies into quality categories. Models such as Linear Regression, Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were employed. Results indicate that ensemble methods (Random Forest) and ANN outperformed linear models, achieving >90% accuracy in classification tasks. This paper provides code snippets, visualizations, and performance metrics to guide stakeholders in adopting ML for water quality monitoring.

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Jay chotaliya, Dr. Santosh Kumar Singh, & Mrs. Amit Kumar Pandey (March-2025). Comparative Analysis of Physicochemical water parameters with reference to Environmental Quality Monitoring using machine learning techniques.. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(3), c334-c339. https://ijnrd.org/papers/IJNRD2503238.pdf

Volume 10 Issue 3, March-2025

Pages : c334-c339

Paper Reg. ID: IJNRD_304683

Published Paper Id: IJNRD2503238

Downloads: 000121992

Research Area: Other area not in list

Author Type: Indian Author

Country: mumbai, maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2503238.pdf

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