A Predictive Model for Water Quality Index Assessment by Machine Learning Approach

The deteriorating state of water quality poses a significant threat to human well-being and ecosystems, fueled by contaminants from industrial discharges, agricultural runoff, and urban activities. This pollution directly impacts human health and disrupts aquatic habitats, jeopardizing biodiversity. Conventional assessment methods fail to address this urgent issue, prompting the advocacy for a novel approach that leverages machine-learning algorithms. This research focuses on developing a predictive model for the Water Quality Index (WQI), a key metric for water quality assessment. Utilizing a diverse dataset from multiple monitoring stations, various machine learning algorithms, including XGBoost, Decision Tree, AdaBoost, K-Neighbours, and Support Vector Machine (SVM), analyze complex relationships among water quality parameters to predict the WQI. The model undergoes rigorous validation, comparing predicted WQI values with independent data, using metrics like accuracy, precision, recall, and F1 score. Sensitivity analysis identifies influential factors, revealing that machine-learning algorithms accurately gauge current water quality and demonstrate the ability to forecast future conditions. This research significantly advances water quality monitoring, emphasizing the vital role of integrating machine learning into environmental management. The implications extend to policymakers, environmental scientists, and water managers, providing valuable insights for formulating effective strategies to protect and enhance water quality.