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Comparison of model selection and data bias on the prediction performance of purpleback flying squid (Sthenoteuthis oualaniensis) fishing ground in the Northwest Indian Ocean

文献类型： 外文期刊

作者： Han, Haibin 1 ; Jiang, Bohui 1 ; Xiang, Delong 1 ; Shi, Yongchuang 1 ; Liu, Siyuan 1 ; Shang, Chen 1 ; Zhao, Xinye 3 ; Zhang, Heng 1 ; Sun, Yuyan 1 ;

作者机构： 1.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Key Lab Ocean & Polar Fisheries, Minist Agr & Rural Affairs, Shanghai 200090, Peoples R China

2.Laoshan Lab Qingdao Marine Sci & Technol Ctr, Qingdao, Peoples R China

3.Shanghai Ocean Univ, Coll Marine Sci, Shanghai, Peoples R China

关键词： Northwest Indian Ocean; Sthenoteuthis oualaniensis; Model prediction performance; Data bias; Visualization of variables importance

期刊名称：ECOLOGICAL INDICATORS （ 影响因子：6.9； 五年影响因子：6.6 ）

ISSN： 1470-160X

年卷期： 2024 年 158 卷

页码：

收录情况： SCI

摘要： Purpleback flying squid (Sthenoteuthis oualaniensis, PFS) is one of the critical economically cephalopod species in the northwest Indian Ocean, and the accurate model and dataset selection are critical parameters for predicting and managing the PFS fishing grounds. In this study, the PFS fishery data for the years 2016-2021 was analyzed using the gravity center of fishing grounds method, generalized additive model (GAM), gradient boosted trees (GBT), 3D Convolutional Neural Network (3DCNN), and 3D Convolutional Neural Networks-Convolutional LSTM Network (3DCNN-ConvLSTM) to explore the differences in annual catches, the annual gravity center of fishing ground, model performance, and importance of environmental variables in the case of datasets A (no moonlight days) and B (no moonlight days + bright moonlight days). The results are as follows: 1) Datasets A and B exhibit similar patterns of variation, with annual catches rising and then declining and the annual gravity center of fishing ground moving northeastward overall; 2) The GAM and GBT models had better model performance on dataset B (GAM (average F1-score +/- standard deviation): 0.678658 +/- 0.014684; GBT: 0.737422 +/- 0.011748) than on dataset A (GAM: 0.676802 +/- 0.013403; GBT: 0.736547 +/- 0.013323), but almost negligible, and the standard deviation of the GAM model on dataset B becomes larger. 3DCNN and 3DCNN-ConvLSTM models perform in contrast, with significantly better F1-score and standard deviations on dataset A (3DCNN: 0.75048 +/- 0.019763; 3DCNN-ConvLSTM: 0.740041 +/- 0.023927) than on dataset B (3DCNN: 0.746378 +/- 0.020337; 3DCNN-ConvLSTM: 0.736927 +/- 0.04498); 3) The 3DCNN (optimal prediction of performance) or GBT (Optimal model stability) model is optimal for predicting the PFS fishing grounds; 4) GBT, 3DCNN, and 3DCNN-ConvLSTM model results all showed that the differences in the importance of environmental variables obtained from datasets A and B were significant; 5) Unlike the GBT model, the 3DCNN and 3DCNN-ConvLSTM models were more susceptible to dataset influences, with significant dependence on environmental variables that have large positive and negative sample differences. This study provides rich suggestions for constructing a predictive model for the PFS fishing grounds in the context of climate change. It also provides a new perspective on cleaning up biased data for light fisheries.

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