Using Artificial Intelligence Techniques in Water Quality Analysis and Prediction: Towards Sustainability

Water is essential to sustain all types of life. However, it is under constant threat of pollution by life itself. Water quality assessment and prediction are critical for environmental management and public health. Artificial Intelligence presents innovative solutions, leveraging advanced algorithms for efficient and accurate analysis and forecasting of water quality. Machine learning classification models are widely used across many industries for predictive analytics. This research aimed to determine the optimal classifier for a water potability dataset. Five commonly used classifiers were assessed: Logistic Regression, Support Vector Machine, Random Forest, XGBoost, and K-Nearest Neighbors. The models were evaluated and compared using precision, recall and F1 scores as key metrics.