Multivariate Water Quality Forecasting of River Ganga Using VAR-LSTM based Hybrid Model

Abstract

Pollution of water is a global environmental crisis faced by many countries. Particularly, India being a developing country, is facing this water pollution across various water bodies such as rivers, ponds, lakes, and groundwater. Further, the river Ganga, the largest river in India, has been highly polluted due to unsustainable industrialization and urbanization across the river belts. Manual water quality measurement and forecasting techniques are time-consuming, tedious, erroneous, and risky. Hence, it is necessary to predict the future quality of water using data-driven approaches for devising strategies to control the pollution levels of the river Ganga. In this paper, a multivariate hybrid model, i.e., Vector Auto Regression - Long Short Term Memory (VAR-LSTM), is proposed for predicting the pollution levels of the river Ganga. The VAR-LSTM is developed by cascading one statistical model, Vector Auto Regression (VAR), with another deep learning model, Long Short Term Memory (LSTM). Here, VAR is used to model the interdependency of various water pollutants using multivariate time series analysis. Subsequently, the fitted values of the VAR model are fed into the LSTM model to explore the temporal feature of the time series water quality data for predicting the water quality. Finally, the proposed hybrid VAR-LSTM model predicts the four water pollutants like Total Coliform (TC), Dissolved Oxygen (DO), Fecal Coliform (FC) and Biological Oxygen Demand (BOD) of river Ganga along with their associated Water Quality Index (WQI). The prediction accuracy of the proposed model is found to be promising.