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

Abstract

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. Comparative analyses showed that ML-based models effectively captured spatial and temporal variations, aligning closely with field measurements. This integration provides a more efficient alternative to traditional methods, which are resource-intensive and less practical for large-scale, remote areas. Our findings demonstrate that this semi-automated technique is a valuable tool for continuous water quality monitoring, particularly in ecologically sensitive areas with limited accessibility. The approach also offers significant applications for climate resilience and policy-making, as it enables timely identification of deteriorating water quality trends that may impact biodiversity and ecosystem health. However, the study acknowledges limitations, including the variability in data availability and the inherent uncertainties in ML predictions for dynamic water systems. Overall, this research contributes to the advancement of water quality monitoring techniques, supporting sustainable environmental management practices and the resilience of the Sundarbans against emerging climate challenges.