A Comparative Analysis of Multivariate Statistical Time Series Models for Water Quality Forecasting of the River Ganga

Water plays an important role in the livelihood of mankind. Hence, water that is used for agriculture, marine culture, human consumption, etc., should be in good condition to minimize the hazardous effect of water pollution on human health. Rapid unsustainable industrialization, improper huge waste disposal, excess amount fertilizer usage, etc., are responsible for the rapid deterioration of the water quality in rivers and other freshwater bodies. Manual continuous water quality measurement is risky, expensive, and time-consuming. Hence, it is essential to forecast the water quality using statistical time series models. In this paper, three widely used statistical multivariate techniques such as Vector Moving Average (VMA), Vector Auto Regression (VAR), and Vector Auto Regression Moving Average (VARMA), are investigated to forecast water quality parameters like Fecal Coliform (FC), Total Coliform (TC), Biological Oxygen Demand (BOD), Dissolved Oxygen (DO), and the associated Water Quality Index (WQI) of the Ganga River. Most of the previous methods worked on forecasting the future values based on past values of individual parameters without considering the interdependency among the water quality parameters. Here, correlation among each parameter is estimated. Subsequently, the future values of a parameter are estimated based on its previous values and the previous values of its correlated parameters. The proposed research work can help properly manage the water quality of the river Ganga by utilizing the forecasted results for the planning of the pollution control strategies. Finally, it helps improve the quality of human beings by minimizing the health issues caused by water pollution.