A CNN-BiLSTM-SVR based Deep Hybrid Model for Water Quality Forecasting of the River Ganga

Water pollution is a serious issue faced not only in India but also in the overall globe. Almost all major Indian rivers are polluted due to the rapid increase in industrialization and poor water quality management. Particularly, the pollution level in the river Ganga has increased significantly. Hence, it is necessary to monitor and manage the pollution levels of the river Ganga using efficient data-driven methods. In this paper, a deep learning-based Convolutional Neural Network - Bidirectional Long Short Term Memory - Support Vector Regression (CNN-BiLSTM-SVR) hybrid model is proposed to forecast the water pollution levels of river Ganga. Four different deep learning models, such as LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM, have been developed as a baseline to compare the performance with that of the proposed model. These models are implemented using water quality data of river Ganga collected from the Uttar Pradesh Pollution Control Board’s official website. Here, only two parameters, i.e., Dissolved Oxygen (DO) and Biochemical Oxygen Demand (BOD), are used in modeling. The proposed CNN-BiLSTM-SVR model provides better forecasting results for two water pollutants, such as DO, BOD, and the associated Water Quality Index (WQI).