An ensemble-driven machine learning framework for enhanced water quality classification

초록

Water quality is a critical factor in ensuring public health and environmental sustainability. Water quality assessment methods, especially concerning their efficiency and accuracy, have become more important with the overwhelming demands on the application of artificial intelligence (AI) as emerging transformative tools. Its accurate definition helps identify health risks, optimize resource consumption, and feed sustainable practices. This study applies machine learning (ML) models to classify water quality using an integrated dataset from Telangana, India. Initial experimentation was done on individual classifiers, which include Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM). Thereafter, an experiment was conducted by merging these classifiers into a voting-based ensemble model and evaluating it using both soft and hard voting strategies to improve predictive performance. It was found that soft voting ensemble resulted in the highest classification accuracy of 96.39%, with strong performance across the other metrics as well, namely, Precision 96.49%, Recall 96.39%, and F1 score 96.41%, The hard voting model and individual base classifiers were also competitive at 95.28% (hard voting), 92.08% for LR, 95% for DT, and 95.66% by using SVM. The soft voting ensemble model, therefore, shows a relative improvement of 1.46% in accuracy over the best-performing base learner and a 27.8% reduction in its error rate. It is, therefore, confirmed that ensemble learning soft voting improves the reliability of water quality classification as well as accuracy, thus providing a strong platform for future environmental monitoring systems.

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