Abstract
In the prediction of algal blooms with the artificial intelligence (AI) technology, the proper selection of the indices is crucial to the quality of the results. Although eutrophication and climate change have been identified as the main contributors to algal blooms, limited knowledge of the relationships among environmental factors hinders the accurate and correct AI prediction. To explore the importance of environmental factors in the occurrence of algal blooms in coastal seas and facilitate the selection of inputs for the prediction model, this study examines observed water quality data and analyzes their relationship using machine learning methods. It was found that nutrients, water temperature, and salinity play important roles in controlling algal blooms in coastal seas, and their influence varies depending on locations. The high-risk cases of algal bloom mainly occur in environments with medium salinity (22–28 psu), particularly in open estuaries with high nutrient levels. According to the feature importance analysis, water temperature presented the most significant impact on algal bloom risk compared to other environmental factors. The variations of water temperature and Chl-a concentration are synchronous when the water temperature is below 32 °C. Additionally, the relationships between inorganic nutrients (NO3−-NO2− and Ortho-P) and Chl-a exhibit asynchronous across multi-temporal scales. The uptake of inorganic nutrients occurs in large quantities within the 24 h prior to the boom in Chl-a concentration. This demonstrates that variations in concentration of inorganic nutrients can be considered as crucial factors in algal bloom predictions, and their significant decrease can be served as an important signal of an impending algal bloom. This study enhances our understanding of the mechanisms behind algal blooms and facilitates their management and prediction strategies in coastal seas.
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