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Related Experiment Video

Updated: Apr 30, 2026

High-Throughput Measurement and Classification of Organic P in Environmental Samples

High-Throughput Measurement and Classification of Organic P in Environmental Samples

Published on: June 8, 2011

Machine learning-driven groundwater quality classification using physicochemical parameters and regulatory

Nisha Kumari Pandit¹, Aniket Anand¹, Sumer Singh Meena²

¹Department of Biotechnology, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144008, India.

Environmental Monitoring and Assessment

|April 28, 2026

Summary

This study developed a machine learning framework for rapid groundwater quality assessment, classifying water as Acceptable, Needs Treatment, or Hazardous. The system provides real-time recommendations for sustainable groundwater management.

Keywords:

Data processing Exploratory data analysis Gradio Logistic Regression Random Forest XGBoost

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Area of Science:

Environmental Science and Engineering
Water Resource Management
Machine Learning Applications

Background:

Groundwater contamination is a significant threat to public health and environmental sustainability, especially in urban areas.
Traditional water quality testing methods are often slow and costly, hindering timely interventions.
Access to safe groundwater is crucial for global sustainable development goals.

Purpose of the Study:

To create a rapid, machine learning-driven framework for classifying groundwater quality based on physicochemical parameters.
To develop an end-to-end decision-support system integrating regulatory guidelines, ML classification, and treatment recommendations.
To provide a scalable and user-friendly tool for sustainable groundwater governance.

Main Methods:

Utilized 5006 groundwater records from India's National Water Quality Monitoring Programme (2018-2022).
Selected key indicators: pH, total dissolved solids (TDS), nitrate, biological oxygen demand (BOD), and total coliform counts.
Employed supervised learning algorithms (XGBoost, Random Forest, SVM, Logistic Regression) after guideline-based labeling into Acceptable, Needs Treatment, and Hazardous classes.

Main Results:

XGBoost demonstrated superior performance with 99.40% accuracy, 0.994 F1-score, and 0.9998 ROC-AUC.
Model validation confirmed robustness and stable predictions across diverse hydrochemical conditions.
A Gradio-based web interface was developed for real-time classification and treatment guidance.

Conclusions:

The proposed machine learning framework offers a rapid and efficient solution for groundwater quality assessment.
The integrated system supports regulatory compliance and informed decision-making for water security.
This approach enhances sustainable groundwater management in rapidly urbanizing regions.