Fisheries forecasting method based on deep learning and canonical correlation analysis
YUAN Hongchun, LIU Hui, ZHANG Shuo, CHEN Guanqi
1.College of Information Technology, Shanghai Ocean University, Shanghai 201306, China; 2.Key Laboratory of Fisheries Information，Ministry of Agriculture and Rural Affairs, Shanghai 201306, China
Abstract: In order to solve the problems of poor performance, difficulty in feature conversion, and insufficient fitting degree in traditional methods of fishing ground prediction, a new fishing situation prediction method—CNN-DNN-CCA(fusion with connection)-RBF model is established based on deep learning and canonical correlation analysis.First, in this method different marine environmental factors were maped into a three-dimensional matrix according to their relative spatial positions within a 5°×5° fishery operation area.Then, the convolutional neural network(CNN)and the deep neural networks(DNN)are used to extract the modal features of the three environmental factors including sea surface temperature, concentration of chlorophyll a, and the sea surface height, and the spatiotemporal factor of fishing grounds.The two feature vectors are fused at the feature level through the canonical correlation analysis(CCA)method.Finally, the fused features were inputted into the radial basis function network(RBF)for classification.The experimental results showed that the fishing ground prediction model based on deep learning and canonical correlation analysis had a recall rate of 90.3% for the South Pacific albacore fishing center, increased by 6.8%-21.8% compared with the random forest(RF), CNN model and DNN model.The new fishing situation prediction model proposed in this study is shown to extract and fuse features automatically through deep learning method and canonical correlation analysis method, and is featured by to elimination of redundant information, simplified feature transformation, and improvement of the operation speed and prediction accuracy.The findings provide a new idea for the fishing ground prediction of albacore tuna.
2021, Vol. 36 
