Integrated analytics and machine learning for water quality index assessment - Monitoring of industrial waste polluted stream

The Water Quality Index (WQI) is a primary metric used to evaluate and categorize surface water quality which plays a crucial role in the management of river water resources. Machine Learning (ML) modeling offers potential insights into water quality index prediction. This study employed advanced ML models to get potential insights into the prediction of water quality index for the Aik-Stream, an industrially polluted natural water resource in Pakistan with 19 input water quality variables aligning them with surrounding land use activities. Six machine learning algorithms, i.e. Adaptive Boosting (AdaBoost), K-Nearest Neighbors (K-NN), Gradient Boosting (GB), Random Forests (RF), Support Vector Regression (SVR), and Bayesian Regression (BR) were employed as benchmark models to predict the Water Quality Index (WQI) values of the polluted stream to achieve our objectives. In the formulation of predictive models, 80% of the dataset was reserved for training, while 20% was set aside for testing. In our comparative analysis of predictive models for water quality index, the Gradient Boost (GB) model stood out for its precision utilizing a combination of just seven parameters (COD, TOC, OG, NH3N, Ar, Ni, Zn), surpassing other models by achieving better results in both training (R2 = 0.88, RMSE = 7.24) and testing (R2 = 0.85, RMSE = 8.67). Analyzing feature importance was also crucial, which showed that all selected variables, except, for NO3 N, TDS and temperature had an impact, on the accuracy of the models predictions. In conclusion, the application of machine learning to assess water quality in polluted environments enhances accuracy and facilitates real-time tracking, enabling proactive risk mitigations.