Intelligent Water Quality Assessment: Predictive Modeling for Potable Water Using Advanced Machine Learning Techniques

Cheni Sruneethi and Bale Rajesh

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 3, 92-98

Abstract: Access to safe drinking water remains a global priority, necessitating robust and accurate methods for potability assessment. This research implements advanced machine learning approaches to develop a predictive framework for water quality classification. The study utilizes a comprehensive dataset containing critical physicochemical parameters including pH levels, sulfate concentration, and trihalomethane content. The methodology encompasses extensive data preparation protocols such as missing value imputation, outlier identification, and feature normalization. Multiple classification algorithms were evaluated to create an optimal prediction model, with performance assessed through rigorous statistical analysis. The developed system was integrated into an interactive web interface using Flask architecture, enabling real-time water quality evaluation based on user-provided parameters. Results demonstrate the system's effectiveness in distinguishing between potable and non-potable water sources, offering significant implications for public health monitoring and environmental management practices.

Keywords: Potable Water Assessment; Predictive Analytics; Classification Algorithms; Feature Significance Analysis; Environmental Monitoring Systems; Public Health Informatics (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST251238 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST251238/IJSRST251238 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i3:id:804

DOI: 10.32628/IJSRST251238

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().