Enhanced remote sensing and deep learning aided water quality detection in the Ganges River, India supporting monitoring of aquatic environments

Water pollution is a pressing global concern, impacting numerous communities across the world. Existing water quality monitoring systems rely on static or periodically collected data, presenting limitations in their ability to provide real-time dynamic insights. This research introduces an innovative approach to address this gap-a dynamic data intake system capable of identifying contamination sources, employing remote sensing techniques to track temporal changes, and issuing timely alerts for safeguarding crucial water resources. The proposed system adopts a hybrid methodology, integrating the QAA-v5 algorithm to derive essential parameters. These parameters serve as input for a pre-trained CatBoost model, which facilitates real-time calculations of chlorophyll-a concentrations at specified geographical coordinates. For future forecasting, the system leverages two distinct models: NBeats and CatBoost Time-Series. Notably, the CatBoost model achieves a commendable regression score of 0.985. For a comprehensive assessment and validation of the system's performance, the research draws upon the dataset provided by the International Ocean-Color Coordinating Group (IOCCG). The innovative framework introduced in this study exhibits considerable promise in advancing water quality protection and monitoring, making a significant contribution to the field of environmental research and management.