Predictive Modeling of Cyanobacterial Blooms and Diurnal Variation Analysis Based on GOCI

Algal bloom is a major ecological and environmental problem caused by abnormal algal reproduction in water, and it poses a serious threat to the aquatic ecosystem, drinking water safety, and public health. Because of the high dynamic and spatiotemporal heterogeneity of bloom outbreaks, the process often presents significant changes in a short time. Therefore, it has important scientific research value and practical application significance to construct an accurate and effective bloom warning model. This study constructs an integrated model combining sequence features, attention mechanisms, and random forest using machine learning algorithms for bloom prediction, based on watercolor geostationary satellite observations and meteorological data from GOCI in South Korea. In the process, high spatial resolution Sentinel-2 satellite data is also utilized for sample extraction. With a 10-m resolution, Sentinel-2 provides more precise spatial information compared to the 500-m resolution of GOCI, which significantly enhances the accuracy of the model, especially in monitoring local water body changes. The experimental results demonstrate that the model exhibits excellent accuracy and stability in the spatiotemporal prediction of water blooms. The average AUC value is 0.88, the F1 score is 0.72, and the accuracy is 0.79 when identifying the dynamic change of water bloom on the hourly scale. At the same time, this study summarized four typical diurnal change modes of effluent bloom, including dispersal mode, persistent outbreak mode, dispersal-regression mode, and subsidence mode, revealing the main characteristics of diurnal dynamic change of bloom. The research results provided strong technical support for water environment monitoring and water quality safety management and showed a good application prospect.