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Effects of Different Spatial Resolutions on Prediction Accuracy of Thunnus alalunga Fishing Ground in Waters Near the Cook Islands Based on Long Short-Term Memory (LSTM) Neural Network Model

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Abstract

Albacore tuna (Thunnus alalunga) is one of the target species of tuna longline fishing, and waters near the Cook Islands are a vital albacore tuna fishing ground. Marine environmental data are usually presented with different spatial resolutions, which leads to different results in tuna fishery prediction. Study on the impact of different spatial resolutions on the prediction accuracy of albacore tuna fishery to select the best spatial resolution can contribute to better management of albacore tuna resources. The nominal catch per unit effort (CPUE) of albacore tuna is calculated according to vessel monitor system (VMS) data collected from Chinese distant-water fishery enterprises from January 1, 2017 to May 31, 2021. A total of 26 spatiotemporal and environmental factors, including temperature, salinity, dissolved oxygen of 0–300 m water layer, chlorophyll-a concentration in the sea surface, sea surface height, month, longitude, and latitude, were selected as variables. The temporal resolution of the variables was daily and the spatial resolutions were set to be 0.5° × 0.5°, 1° × 1°, 2° × 2°, and 5° × 5°. The relationship between the nominal CPUE and each individual factor was analyzed to remove the factors irrelavant to the nominal CPUE, together with a multicollinearity diagnosis on the factors to remove factors highly related to the other factors within the four spatial resolutions. The relationship models between CPUE and spatiotemporal and environmental factors by four spatial resolutions were established based on the long short-term memory (LSTM) neural network model. The mean absolute error (MAE) and root mean square error (RMSE) were used to analyze the fitness and accuracy of the models, and to determine the effects of different spatial resolutions on the prediction accuracy of the albacore tuna fishing ground. The results show the resolution of 1 ° × 1° can lead to the best prediction accuracy, with the MAE and RMSE being 0.0268 and 0.0452 respectively, followed by 0.5° × 0.5°, 2° × 2° and 5° × 5° with declining prediction accuracy. The results suggested that 1) albacore tuna fishing ground can be predicted by LSTM; 2) the VMS records the data in detail and can be used scientifically to calculate the CPUE; 3) correlation analysis, and multicollinearity diagnosis are necessary to improve the prediction accuracy of the model; 4) the spatial resolution should be 1 ° × 1 ° in the forecast of albacore tuna fishing ground in waters near the Cook Islands.

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Acknowledgements

We thank Liancheng Overseas Fishery (Shenzhen) Co., Ltd., for providing VMS data. This research was supported by the National Natural Science Foundation of China (No. 32273185), the National Key R&D Program of China (No. 2020YFD0901205), and the Marine Fishery Resources Investigation and Exploration Program of the Ministry of Agriculture and Rural Affairs of China in 2021 (No. D-8006-21-0215). Gratitude also goes to Dr. Huihui Shen, School of Foreign Languages, Shanghai Ocean University for improving the manuscript.

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Authors and Affiliations

  1. College of Marine Sciences, Shanghai Ocean University, Shanghai, 201306, China

    Hui Xu, Liming Song, Yuwei Li & Min Zhang

  2. National Engineering Research Center for Oceanic Fisheries, Shanghai, 201306, China

    Liming Song, Yuwei Li & Min Zhang

  3. College of Information Technology, Shanghai Ocean University, Shanghai, 201306, China

    Tianjiao Zhang

  4. Liancheng Overseas Fishery (Shenzhen) Group, Shenzhen, 518035, China

    Jieran Shen

  5. College of Information Engineering, Shanghai Maritime University, Shanghai, 200135, China

    Kangdi Li

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  1. Hui Xu
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Xu, H., Song, L., Zhang, T. et al. Effects of Different Spatial Resolutions on Prediction Accuracy of Thunnus alalunga Fishing Ground in Waters Near the Cook Islands Based on Long Short-Term Memory (LSTM) Neural Network Model. J. Ocean Univ. China 22, 1427–1438 (2023). https://doi.org/10.1007/s11802-023-5525-5

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