Short to medium-term forecasting of fishing ground distribution based on deep learning model

Most of the fishing ground research focuses on real-time predictions and lacks continuous forecasting for future changes over a certain period. Traditional models characterized by large, single temporal scales lack effectiveness in accounting for the autocorrelation of environmental factors. Deep learning has demonstrated superior performance and promising development prospects due to its accurate and efficient ability to mine nonlinear information in the era of big data. Therefore, we take Ommastrephes bartramii as an example and constructed 28 different temporal scales and lead periods cases based on U-Net and compared them with GAM, NN and ConvLSTM model results. The input factors of this model are sea surface temperature (SST) and the output factors are the center fishing ground data (1998-2019). The results reveal that the optimal temporal scale and lead period for this model is 15 days and 4 on U-Net. The SST fluctuation information between different lead periods of environmental field and the coupling degree in fishing grounds may be essential factors affecting the model performance differences. It enhances marine fisheries understanding from artificial intelligence.