Forecasting seasonal TDS using a normalized difference water index in the Ganges River in Bangladesh

Abstract

The Ganges is one of the most remarkable rivers in Bangladesh. The river contributes to the surface water supply, which is significantly high in the northwest region of Bangladesh. The water quality of this river changed based on many environmental events, and rainfall was one of them. The research focused on finding the mathematical relation between the field observations of water quality parameters and the observed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. This study treated the Total Dissolved Solids (TDS) as one of the crucial parameters in the water quality index, which was monitored and forecasted using multiple linear regression from satellite-derived Normalized Difference Water Index (NDWI) from the Landsat imageries and Electrical Conductivity (EC) from the field observations for the pre-monsoon, monsoon, and post-monsoon periods. The predicted values returned significant correlation coefficient values compared to the field TDS values of 0.64, 0.71, and 0.52 for the three periods, respectively. The regression equations found from this study may be further utilized to predict the TDS values in the future years.