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

Abstract

Water pollution is a global problem. In developing countries like India, water pollution is growing exponentially due to faster unsustainable industrial developments. Recently, the river Ganga has been polluted faster and caused lots of diseases among humans and aqua-animals. Hence, continuous water quality monitoring with appropriate water quality management plans is required to maintain sustainable growth. The manual methods of water quality analysis are not suitable in order to get the proper results due to the involvement of life risk and high time consumption. Therefore, it is essential to move towards some advanced data collection, processing, and monitoring approaches that are easy, less costly, and fast. This can be achieved by using data-driven approaches like deep learning techniques due to their strong decision-making ability and automatically learning capabilities from their experience. Hence, a deep hybrid model using Convolutional Neural Networks - Gated Recurrent Units - Support Vector Regression (CNN-GRU-SVR) is proposed to forecast the water quality of the river Ganga using historical data. Here, only two crucial available water pollutants, such as dissolved oxygen and biochemical oxygen demand, collected from Uttar Pradesh Pollution Control Board’s official website, are considered for forecasting. The effectiveness of the proposed model is experimentally established by comparing the results with that of the five different deep learning models that have been developed as baseline models.