Channel: Marine Environmental Protection http://www.jto.ac.cn EN-US http://www.jto.ac.cn/EN/1009-5470/current.shtml http://www.jto.ac.cn 1009-5470 http://www.jto.ac.cn/EN/10.11978/2021156 We analyzed the principal components of hydrology, water quality, and meteorological data in Pingtan, Fujian province from 2013 to 2019. We selected 5 meteorological factors and 4 water quality factors. Our study establishes four early-warning model, KNN (K-nearest neighbor), RF (random forest), GBRT (gradient-boosted regression trees), Bagging (bootstrap aggregating) with meteorological factors and water quality factors as input indicators, and algal cell density as output indicators. After normalizing the 802 sets of marine monitoring data from 2013 to 2019, 80% of the data were randomly selected as the model training samples, and the remaining 20% were used as data of model verification. When temperature, wind speed, sea level pressure, and chlorophyll a are used as input parameters, the calculation result of KNN regression model is more accurate (R²=0.624, RMSE=0.821 μg·L^-1, MAE=0.836 μg·L^-1). In the sea area without chlorophyll a monitoring index, a BP neural network early-warning model with chlorophyll a concentration as the output index and temperature, sunshine, wind speed and AOI as input parameters was established, which has better warning accuracy (R²=0.651, RMSE=0.062 μg·L^-1, MAE=0.033 μg·L^-1). Our results can provide a reference for the red tide early warning research in the Pingtan coastal area.

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