Water Quality Classification in Terms of WQI Using Machine Learning Algorithms in Keenjhar Lake, Pakistan

Water quality management is critical for sustainable resource utilization, public health protection, and environmental conservation. This study employs machine learning techniques to enhance the water quality classification (WQC) for Keenjhar Lake, Karachi, Pakistan, using a comprehensive dataset spanning from 1993 to 2022. Three machine learning models, the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) algorithms, were evaluated for their performance on a dataset comprising 360 monthly instances and 6 key water quality parameters: temperature, pH, dissolved oxygen, electrical conductivity, BOD5, nitrate, fecal coliform, and total coliform. The models were assessed using statistical metrics, including accuracy, precision, recall, Pearson’s correlation, and F1-score. Among the models, the SVM algorithm demonstrated superior performance, achieving a classification accuracy of 99.50% for WQC. This study highlights the integration of machine learning in water quality analysis as a novel approach for precise classification, offering insights into improved decision-making for water resource management and treatment strategies.