Construction and Comparison of Machine-Learning Forecast Models of Albacore Thunnus alalunga Fishing Grounds in the South Pacific Ocean

文献类型： 外文期刊

第一作者： Li, Jianxiong

作者： Li, Jianxiong;Chen, Feng;Li, Jianxiong;Chen, Feng;Dai, Qian;Zhu, Wenbin;Li, Dewei;Chen, Feng;Dai, Qian;Zhu, Wenbin;Li, Dewei;Yu, Wei;Zhou, Weifeng

作者机构：

关键词： albacore; fishing ground prediction; unsupervised learning; machine learning; discretization; south pacific

期刊名称：FISHES （ 影响因子：2.4； 五年影响因子：2.4 ）

ISSN：

年卷期： 2024 年 9 卷 10 期

页码：

收录情况： SCI

摘要： The traditional methods for predicting the distribution of albacore (Thunnus alalunga) fishing grounds have low performance and accuracy. Uneven sampling can result in unreasonable evaluation indicators. To address these issues, three methods, equi-frequency, K-means clustering algorithm, and 1-R split, were applied to discretize the catch per unit effort (CPUE) of albacore in the South Pacific from 2016 to 2021 and partition the fishing grounds into abundance levels. Eight machine learning models were used to predict the fishing grounds. In addition to the traditional evaluation index based on confusion matrix, top-k index was also used to evaluate the accuracy of fishery abundance predictions. The results showed that (1) When sampling is unbalanced, the reported accuracy does not fully represent the actual performance of the model in predicting the abundance of albacore in the fishing ground. F1 value can be used as the index of the model effect and stability. (2) In binary classification, the quartile stacking algorithm has the best stacking performance, with F1 0.89. (3) The top-1 prediction accuracy of three-category fishery forecasting is the highest at 0.74, and the top-1 prediction accuracy of five-category fishery forecasting is the highest at 0.54. (4) The top-k accuracy of classification of fisheries with multiple abundance using K-means is significantly better than that of equal frequency discretization (p < 0.001). The top-k evaluation index was used to predict the fishing grounds of albacore across multiple abundance levels for the first time in this study, which is significant for pioneering a new method for this application and which provides a demonstration of the development of artificial intelligence techniques for fisheries in the future.

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