Volume 20 Issue 2
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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
shu

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

  • 1.

    College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai 201306, China

  • 2.

    Collaborative Innovation Center for National Distant-Water Fisheries, Shanghai 201306, China

  • 3.

    Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China

  • 4.

    National Distant-Water Fisheries Engineering Research Center, Shanghai 201306, China

  • 5.

    Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture and Rural Affairs, Shanghai 201306, China

  • 6.

    School of Education, Shangdong Women's University, Jinan 250300, China

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  • Received Date: October 05, 2023
  • Revised Date: October 31, 2023
  • Accepted Date: December 03, 2023
  • Available Online: December 06, 2023
    Graphical Abstract
    • Abstract

  • 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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