As a marine disaster,red tides have a serious impact on marine fisheries,ecology,economy,human production and life.Red tides have been widely concerned by researchers for a long time.However,due to its complex formation mechanism,red tide forecasting is extremely challenging.Aiming at addressing problem of red tide forecasting,this paper collects the marine monitoring data before and after the occurrence of red tide in Xiamen sea area,and analyzes the correlation between multiple environmental factors and the red tide occurrence by combining the methods of Pearson correlation coefficient,Scatter matrix,and multiple correlation coefficient.The fusion method of LSTM and CNN based on deep learning are applied to mine the temporal dependence of environmental factors and find the local features of sequence data,then predict the occurrence of red tides.In the Xiamen No.1 and Xiamen No.2 datasets,the RMSE and MAE errors of this method are reaching 0.5218 and 0.5043,respectively.The forecast probability of red tide occurrence was further determined through the collaborative comparison model.The final forecast accuracy of the two datasets is 67.58%and 63.49%,respectively.This study provides exploratory experience for the analysis and forecasting of red tides,which proves the feasibility of applying deep learning methods to red tide forecasting.
