Abstract
Improving water quality is vital for enhancing public health, productivity, and economic prosperity. However, many water bodies that supply essential resources for domestic, agricultural, and industrial use are increasingly contaminated, necessitating innovative water management technologies combined with effective monitoring systems. This study explores the application of Artificial Intelligence (AI) as a central tool to address the urgent challenges posed by global water scarcity and the complexities of Water Resources Management (WRM). Leveraging advanced AI methodologies, including Machine Learning (ML) and predictive analytics, the research aims to provide actionable insights for optimizing water usage and improving forecasting accuracy. The ML models, specifically the Extra Trees Regressor (ET) and K-Nearest Neighbors (KNN), were explored to improve decision-making in WRM. The primary goal was to develop models with high predictive accuracy and ensure interpretability and cost-effectiveness for real-world applications. A comprehensive feature selection strategy was employed, utilizing sequential feature selection and the ET model’s built-in feature importance function to identify critical predictors influencing water quality outcomes. The results demonstrated that while the ET model consistently outperformed KNN, achieving an R² score of 0.859 when using the full feature set, reducing the model to ET’s suggested top four features—flow, electrical conductivity, turbidity, and temperature—significantly lowered its R² score. This underscores the limitations of ET's reduced feature subset, which contrasted sharply with the robust sequential feature selection method that recommended using all features for improved predictive performance. The study further employed SHAP (SHapley Additive Explanations) to analyze feature contributions, which is crucial for stakeholder trust and informed decision-making. The findings underscore the importance of balancing accuracy, monitoring cost, and interpretability of machine learning applications within WRM. With a strong emphasis on AI-driven decision-making, this research underscores the transformative potential of AI to foster sustainability in water management and deliver innovative solutions adaptable to diverse geographical regions. In doing so, it positions AI not only as a predictive tool but as an integrated approach that enhances water security, setting a framework for future applications across various sectors. This comprehensive approach to AI in WRM reinforces its role as a cornerstone technology in achieving long-term water sustainability and resilience.