Suspended Sediment Concentration Analysis Using Remote Sensing and Machine Learning Approach

This study employs remote sensing technology to thoroughly analyse sediment dynamics in expansive aquatic environments, with a specific focus on the Ganga River basin. The investigation spans from 2007 to 2011, utilizing Medium Resolution Imaging Spectrometer (MODIS) MYD09A1.061 Aqua Surface Reflectance 8-Day Global data to assess Suspended Sediment Concentration . By integrating ground-based silt data with satellite data, the study captures temporal variations in suspended sediment levels. The Google Earth Engine (GEE) platform was employed to process sensor imagery and calculate reflectance data, enabling accurate computations for specific time intervals. To further analyse the data, Support Vector Regression (SVR) model was developed. This model analyse changes in reflectance data  to corresponding  observed silt measurements, providing insights into sediment behavior. The results from this model are presented using 2D graphs, highlighting the  effectiveness of remote sensing technology in understanding the sediment dynamics in large river systems. This research offers significant advancements in  methods for monitoring and maintaining water quality in aquatic environments.