Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans.

This study presents a semi-automated approach for assessing water quality in the Sundarbans, a critical and vulnerable ecosystem, using machine learning (ML) models integrated with field and remotely-sensed data. Key water quality parameters-Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, and pH-were predicted through ML algorithms and interpolated using the Empirical Bayesian Kriging (EBK) model in ArcGIS Pro. The predictive framework leverages Google Earth Engine (GEE) and AutoML, utilizing deep learning libraries to create dynamic, adaptive models that enhance prediction accuracy.