Prediction on fishing ground of Ommastrephes bartramii in Northwest Pacific based on deep learning

Neon flying squid (Ommastrephes bartramii) is a primary economic cephalopod species in the Northwest Pacific Ocean. Accurate identification the spatial distribution of fishing ground provides a scientifically sound and effective foundation for fishery production. In the era of big data in marine fisheries and marine remote sensing, extracting and mining valuable information from vast datasets has emerged as a significant challenge in forecasting fishing grounds. Consequently, this study utilizes the theories of deep learning and fisheries oceanography, utilizing sea surface temperature (SST) data as input to develop a U-Net model for discriminating central fishing grounds from July to November in 1998-2019. The results indicate an accuracy of 86.7% for the validation set, 89.7% for the training set, and the accuracy, precision, recall and balanced F1-score values for the 2020 test set being 87.2%, 0.91, 0.87 and 0.89, respectively. The catch data from fisheries is largely consistent with the predicted central fishing grounds, and the model's application proved effective. Across various climatic conditions, the model demonstrates robust adaptability. The latitude of the central fishery shifts southward during El Niño event and shifts northward during La Niña events. The model constructed in this study can effectively address the problem of fishery discrimination under complex data set, improve the precision of fishing ground prediction models, and lay a theoretical basis and foundation for the realization of fishing ground prediction. It holds promising application prospects.