Integrating remote sensing and machine learning to detect turbidity anomalies in hydroelectric reservoirs

Monitoring water quality in reservoirs is essential for the maintenance of aquatic ecosystems and socioeconomic services. In this scenario, the observation of abrupt elevations of physicochemical parameters, such as turbidity and other indicators, can signal anomalies associated with the occurrence of critical events, requiring operational actions and planning to mitigate negative environmental impacts on water resources. This work aims to integrate Machine Learning methods specialized in anomaly detection with data obtained from remote sensing images to identify with high turbidity events in the surface water of the Três Marias Hydroelectric Reservoir. Four distinct threshold-based scenarios were evaluated, in which the overall performance, based on F1-score, showed decreasing trends as the thresholds became more restrictive. In general, the anomaly identification maps generated through the models ratified the applicability of the methods in the diagnosis of surface water in reservoirs in distinct hydrological contexts (dry and wet), effectively identifying locations with anomalous turbidity values. [Display omitted] •Detection of turbidity anomalies in surface water with optimized performance metrics•Evaluation of four turbidity scenarios (25, 14, 10, and 6 NTU) for the detection of anomalies•Extraction of remote sensing data for training of Machine Learning models through Google Earth Engine services•Turbidity anomaly maps produced for different hydrological contexts (dry and wet seasons)