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Estimating Spatiotemporal Fishing Effort of Trawlers with Vessel-Monitoring System Data: A Case Study of the Sea Area of the Bohai Sea and the Yellow Sea, China

文献类型： 外文期刊

作者： Li, Dan 1 ; Lu, Feng 1 ; Xu, Shuo 1 ; Liu, Huiyuan 1 ; Xue, Muhan 1 ; Cui, Guohui 1 ; Ma, Zhenhua 2 ; Fang, Hui 3 ; Wang, Yu 1 ; Tiralongo, Francesco 1 ;

作者机构： 1.Chinese Acad Fishery Sci, Fisheries Engn Inst, Beijing 100141, Peoples R China

2.Chinese Acad Fishery Sci, South China Sea Fisheries Res Inst, Guangzhou 510300, Peoples R China

3.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Shanghai 200090, Peoples R China

关键词： fisheries; trawling; navigation data; machine-learning algorithm; fishing behavior

期刊名称：JOURNAL OF MARINE SCIENCE AND ENGINEERING （ 影响因子：2.9； 五年影响因子：2.9 ）

ISSN：

年卷期： 2024 年 12 卷 1 期

页码：

收录情况： SCI

摘要： Measuring the distribution of the fishing effort of trawlers is of great significance for describing marine fishery activities, quantifying fishing systems in terms of marine ecological pressure, and revising the regulations of fishing. The purpose of this paper is to develop an efficient learning algorithm to detect the fishing behavior of trawlers to analyze the distribution of fishing effort. The vessel-monitoring system data of more than 4600 trawlers from September 2019 to April 2023 were used for feature extraction. According to the spatiotemporal information provided by the vessel position data, 11-dimensional features were extracted to form the feature vectors. A Slime Mould Algorithm-optimized Light Gradient-Boosting Machine (SMA-LightGBM) algorithm was proposed to classify the feature vectors to recognize fishing behavior. The presented method showed a remarkable generalization ability and high accuracy, sensitivity, specificity, and Matthews correlation coefficient in the test results, with scores of 98.23%, 98.75%, 97.75%, and 0.9646, respectively. Subsequently, the trained model was used to identify the fishing behavior of trawlers belonging to the coastal provinces of the Bohai Sea and the Yellow Sea in the sea area of 117 degrees E similar to 132 degrees E, 26 degrees N similar to 41 degrees N. The fishing effort was calculated and evaluated according to the fishing behavior recognition results. The mean absolute error was 0.3031 kW center dot h, and the coefficient of determination score was 0.9772. The thermal map of the fishing effort of the trawler was mapped, and the spatiotemporal characteristics were estimated in the region of interest from 2019 to 2023 with a spatial resolution of 1/8 degree x 1/8 degree. This method is an efficient way of analyzing the spatiotemporal characteristics of the fishing effort of trawlers. It provides a quantitative basis for the assessment of fishery resources and can inform fishing policies.

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