Forecasting of Water Quality for the River Ganga using Univariate Time-series Models

Abstract

Water problem is one of the important issues faced across globe, particularly developing countries like India. Hence, there is a need for continuous monitoring and forecasting of water quality with the most advanced techniques having low implementation cost, less time consumption as well as high accuracy. This will help the concerned authorities and governments to plan and implement necessary steps to improve the quality of the water, particularly freshwater available in the rivers. Specifically, the water quality of the river Ganga has been deteriorated to a great extent and requires continuous monitoring as well as forecasting of water pollutants to help in water quality management. Hence, in this article, three widely used time series-based models such as Auto-Regressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Prophet have been implemented to predict the water quality of the river Ganga. Here, the models are developed on the Uttar Pradesh Pollution Control Board’s official data for the river Ganga corresponding to nine water quality monitoring stations situated in Uttar Pradesh. Further, only two important water parameters such as dissolved oxygen and biochemical oxygen demand, are considered for prediction and subsequently for the forecasting of the water quality. The experimental analysis concludes that SARIMA and Prophet model predict the water quality parameters as well as Water Quality Index (WQI) more accurately.