Development of entropy-river water quality index for predicting water quality classification through machine learning approach

Monitoring of river water is necessary to reveal its quality and pollution level so that we can protect human health and the environment. The present study explored the water quality of the Narmada River in India. To evaluate the water quality of the Narmada River, water samples were collected from 13 sites during the pre- and post-monsoon seasons, and were analyzed for different physicochemical parameters. The results from the analysis were used for the development of the entropy-river water quality index (ERWQI). The ERWQI was used to estimate the Narmada river water quality for two different uses: drinking after disinfection (ERWQI(d)) and bathing (ERWQI(b)). The machine-learning-based classification models, namely the Logistic regression (LR), Support Vector (SV), K-Nearest Neighbor (KNN), Random Forest (RF), and Gradient Boosting (GB) models were examined to predict and classify ERWQI. The precision, recall, F1 score, and confusion matrix were used to evaluate the performance of the model. The findings of this study identified the LR model as the most accurate classification model with the highest accuracy score for both the ERWQI(d) and ERWQI(b). Moreover, this study also revealed that the water quality of the Narmada River was unsuitable for drinking after disinfection and hence, before any further use it requires treatment through conventional or an advanced techniques. However, the ERWQI(b) of the Narmada River was categorized as excellent to fair. This study has broad implications for the classification of river water quality and can provide some very useful information to monitoring agencies and policymakers.