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RESEARCH ARTICLE   (Open Access)
Contents Vol 2 (1)

Machine Learning-Driven Water Quality Index Prediction: Enhancing Accuracy with Gradient Boosting and Explainable AI for Sustainable Water Monitoring

Md. Jahidul Islam1, Siraj Us Salekin2, Asif Anzum3, Nafis Zaman1, Abdullah Al Ahad Khan4, Dilip Sarkar5, Md. Liton Rabbani6, Md. Tarek Hossain6

+ Author Affiliations

Applied Agriculture Sciences 2 (1) 1-14 https://doi.org/10.25163/agriculture.2110031

Submitted: 12 August 2024 Revised: 06 October 2024  Published: 07 October 2024 

Abstract

Background: Water is fundamental to the survival of all life forms, yet access to clean and safe water remains a critical challenge worldwide. Contaminated water is a significant contributor to waterborne diseases, highlighting the need for effective water quality monitoring. The Water Quality Index (WQI) is a standard tool for assessing water quality; however, traditional WQI methods are often constrained by inconsistencies, laboratory inaccuracies, and human error. Methods: This study aimed to overcome these limitations by integrating advanced machine learning (ML) techniques into WQI prediction. Physicochemical parameters, including pH, chloride (Cl), sulfate (SO4²), sodium (Na), potassium (K), calcium (Ca²), magnesium (Mg²), total hardness, and total dissolved solids, were collected from diverse water sources to form a robust dataset. ML algorithms such as Gradient Boosting, Random Forest, and XGBoost, augmented with explainable AI (XAI), were employed to enhance prediction accuracy. The dataset was split into training (70%), testing (15%), and validation (15%) subsets, and model performance was assessed using RMSE, MSE, MAE, and R² metrics. Results: Gradient Boosting outperformed other models, achieving 96% accuracy on the test dataset after fine-tuning. It demonstrated superior predictive capabilities, as evidenced by its performance metrics. These results indicate the potential for ML techniques to address the limitations of traditional WQI methods. Conclusion: This study demonstrates the effectiveness of ML-driven approaches in improving water quality assessments. The integration of Gradient Boosting and explainable AI provides a reliable framework for WQI prediction, enabling better decision-making in environmental health policies and water resource management. This approach offers a pathway to more efficient and accurate water quality monitoring systems.

Keywords: Water Quality Index (WQI), Water Quality Monitoring, Machine Learning Algorithms, Explainable AI (XAI), Predictive Modelling

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