浊度
环境科学
水力发电
水力发电
水资源
水质
水文学(农业)
地表水
遥感
异常检测
异常(物理)
水位
水资源管理
环境工程
计算机科学
地质学
地图学
人工智能
地理
工程类
生态学
海洋学
物理
岩土工程
凝聚态物理
生物
电气工程
作者
Anderson Paulino Souza,Bruno Oliveira,Mauren Louise Sguario Coelho de Andrade,Maria Clara V.M. Starling,Alexandre H. Pereira,Philippe Maillard,Keiller Nogueira,Jefersson A. dos Santos,Camila C. Amorim
标识
DOI:10.1016/j.scitotenv.2023.165964
摘要
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.
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