Published Paper Details:

Predicting water quality is essential for ensuring public health and sustainable water resource management. This study explores the application of machine learning algorithms, specifically Random Forest (RF) and Naive Bayes (NB), for effective water quality prediction. Using a dataset composed of various physicochemical parameters, we analyze and classify water quality indicators to assess its suitability for consumption and environmental health. Random Forest, an ensemble learning method, is leveraged for its robustness in handling large datasets and its ability to capture complex patterns in water quality features. Naive Bayes, a probabilistic classifier, complements this by providing a simple yet effective approach to classify water quality based on conditional probabilities. Both models are evaluated in terms of accuracy, precision, recall, and F1-score, with comparative analysis to highlight their strengths and limitations. The results demonstrate that combining the predictive accuracy of Random Forest with the interpretability of Naive Bayes offers a practical approach for water quality monitoring, supporting real-time decision-making and regulatory compliance in water resource management.