Water Quality Assessment for River Ganga in Kanpur City, Uttar Pradesh, India using Machine Learning model

The river Ganga, revered as a lifeline of North India, is facing severe water quality degradation due to various man-made activities. The city of Kanpur, Uttar Pradesh, India, located on the west bank of the river Ganga, is known for its leather industrial area. The city produces approximately 450 million litres of municipal sewage and industrial effluent, most of this flowing directly into the holy river Ganga. Due to this, the Ganga River basin pollution is increasing daily, and to overcome this problem scientifically, there is a need to develop different models for water quality, which has been growing in recent years. The key focus of the study is to determine the Water Quality Index (WQI) along with developing a model to assess the changes in water quality over a decade in the Ganga River basin in Kanpur City. The dataset includes monthly measurements (2015-2023) of pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total coliform at seven distinct locations in Kanpur city, encompassing both upstream and downstream areas. Artificial Neural Networks (ANNs) were employed to construct a model to pretend, along with forecasting, the levels of dissolved oxygen (DO). The model has a notable level of accuracy in predicting dissolved oxygen (DO). The scatter plots comparing the actual and anticipated levels of dissolved oxygen (DO) during the training and testing periods exhibited a strong coefficient of determination (R2) with values of 0.90 and 0.92, respectively. Results show that the ANN model can predict water quality effects in advance, and thus, it helps to take protective actions to preserve the river free from pollution. Keywords: Water Quality, River Ganga, Artificial Neural Network, Dissolved Oxygen.