Advancing Water Quality Management: An Integrated Approach Using Ensemble Machine Learning and Real-Time Interactive Visualization

ABSTRACT Access to safe and potable water is critical for public health and socioeconomic development. Traditional methods for predicting water quality often lack accuracy and robustness due to limitations in data availability and model complexity. This study presents a novel approach to predicting water potability by developing an advanced ensemble model and an interactive visualization dashboard. A comprehensive dataset of water quality parameters was collected and preprocessed to ensure data integrity. An ensemble model combining Decision Trees, Random Forest, Gradient Boosting Machines (GBM), XGBoost and Neural Networks was constructed, leveraging the strengths of each algorithm to enhance predictive accuracy. The model achieved an accuracy of 96.7%, precision of 96.7%, recall of 100%, and F1-score of 98.4%, outperforming existing models in the literature. Furthermore, to address contemporary developments in the field of machine learning, a transformer-based model was introduced alongside the existing ensemble and neural network approaches. This integration reflects the heightened prominence of deep learning methods and aligns the study’s methodology with cutting-edge research trends. Cross-validation results further confirmed the model’s robustness, with a mean accuracy of 96.9% and a low standard deviation. The interactive Water Quality Predictive Dashboard developed using the Dash framework, provides real-time predictions and visualizations, allowing stakeholders to input new data and receive immediate feedback on water potability. This tool enhances user accessibility and supports informed decision-making. The study highlights the advantages of the ensemble approach in improving prediction accuracy and reliability, though it also acknowledges the complexity and data dependency challenges. Overall, the developed model and dashboard offer a powerful solution for water quality monitoring with broad applicability for enhancing public health and resource management.