DING Peng, ZOU Xiaorong, DING Shuyi, BAI Siqi. Study on relationship between Thunnus albacares catches and climatic factors based on CNN-BiLSTM model[J]. South China Fisheries Science, 2024, 20(2): 19-26. DOI: 10.12131/20230190
Citation: DING Peng, ZOU Xiaorong, DING Shuyi, BAI Siqi. Study on relationship between Thunnus albacares catches and climatic factors based on CNN-BiLSTM model[J]. South China Fisheries Science, 2024, 20(2): 19-26. DOI: 10.12131/20230190

Study on relationship between Thunnus albacares catches and climatic factors based on CNN-BiLSTM model

More Information
  • Received Date: October 05, 2023
  • Revised Date: October 31, 2023
  • Accepted Date: December 03, 2023
  • Available Online: December 06, 2023
  • To explore the impact of climatic factors on Thunnus albacares catches, we studied its relationship with low-frequency climatic factors by using correlation analysis, BP neural network, LSTM model, BiLSTM model and CNN-BiLSTM model based on the data of nine climate factors, including Southern Oscillation Index (SOI), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), North Pacific Index (NPI), global sea-air temperature anomaly index (dT), El Niño-related indexes (Niño1+2, Niño3, Niño4, Niño3.4) from 1960 to 2021, as well as global T. albacares catches data. The results show that the importance of climate change characterization factors on T. albacares catches followed a descending order of dT>SOI>Niño1+2>PDO>NPI>NAO, whose corresponding optimal lag periods were 0, 11, 6, 5, 15 and 0 years, respectively. CNN-BiLSTM model had the highest prediction accuracy, followed by BiLSTM, LSTM and BP. The goodness of fit between the predicted and actual values of CNN-BiLSTM model was 0.887, with a mean absolute error of 0.125 and a root mean square error of 0.154. The trend of predicted values and actual values was basically consistent, indicating a good model fitting effect.

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