Machine Learning-Based Multifaceted Analysis Framework for Comparing and Selecting Water Quality Indices

Water quality is essential to the population's well-being, water resources management, and environmental development strategies. In this article, we propose a framework based on machine learning (ML) techniques for enhancing the assessment of water quality based on water quality indices (WQIs). It consists of three algorithms that could serve as a foundation for automating the evaluation of any resource based on indices and can operate locally or globally. Local-level algorithms assist in selecting suitable WQIs tailored to specific water sources and quality requirements, while global-level algorithm evaluates WQI robustness across diverse water sources. We also provide a warning system to mitigate differences in water quality evaluation using WQIs and a valuable tool (based on the features' importance) for selecting ML models that prioritize the water parameters' significance. The framework's design draws upon conclusions from a case study involving the forecast and comparison of two WQIs for the Brahmaputra River. Any other data series, WQIs, and water parameters can be employed.