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ARIMA模型在预测长江靖江段沿岸鱼类渔获量时间格局中的应用
引用本文:鲁泉, 方舟, 李楠, 陈新军. 以灰色系统理论 (GM) 模型为基础构建印度洋捕捞渔获量预测模型[J]. 水产学报, 2023, 47(6): 069303. DOI: 10.11964/jfc.20210312700
作者姓名:鲁泉  方舟  李楠  陈新军
作者单位:1.上海海洋大学海洋科学学院,上海 201306;2.农业农村部渔业渔政管理局,北京 100125;3.上海海洋大学,大洋渔业资源可持续开发教育部重点实验室,上海 201306;4.上海海洋大学,国家远洋渔业工程技术研究中心,上海 201306;5.上海海洋大学,农业农村部大洋渔业开发重点实验室,上海 201306
基金项目:国家重点研发计划(2019YFD0901404);上海市科技创新行动计划(19DZ1207502)
摘    要:为了建立捕捞渔获量预测模型,实验利用2000—2016年印度洋渔获量数据,采用灰色系统理论方法,分析了影响其总渔获量的主要渔获类别,建立多种GM模型 (Grey model)并进行比较,同时利用2017年与2018年的数据进行验证,得到的最优GM模型用来预测2019—2025年印度洋总捕捞渔获量。结果显示,影响印度洋总渔获量的主要类别有底层鱼类、甲壳类、中上层鱼类、其他海洋鱼类和头足类,其灰色关联度均在0.70以上,经过筛选得到的最优预测模型为GM (1, 5)和GM (1, 6),平均相对误差分别为1.83%和1.90%,灰色关联度均在0.9以上。2017年和2018年预测平均相对误差分别为3.78%和3.42%。2019—2020、2021—2025年印度洋总渔获量预测值分别为1 186万~1 290万t、1 227万~1 324万t,其主要渔获量增加可能来自中上层鱼类、头足类以及底层鱼类等。研究表明,2021—2025年印度洋总渔获量的增长幅度有限,总增长量在80万t以内,基本处于充分开发阶段,建议未来应严格控制渔业发展规模,确保印度洋海洋渔业的可持续发展和渔业资源可持续利用。

关 键 词:捕捞渔获量  灰色关联  GM (1   N) 模型  印度洋
收稿时间:2021-03-22
修稿时间:2021-05-28

ARIMA model application to predict temporal pattern of fish catches of coastal area at Jingjiang Reach of the Yangtze River
LU Quan, FANG Zhou, LI Nan, CHEN Xinjun. Prediction model of marine catch based on GM (1, N) in the Indian Ocean[J]. Journal of fisheries of china, 2023, 47(6): 069303. DOI: 10.11964/jfc.20210312700
Authors:LU Quan  FANG Zhou  LI Nan  CHEN Xinjun
Affiliation:1.College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;2.Fisheries Bureau, Ministry of Agriculture and Rural Affairs, Beijing 100125, China;3.Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China;4.National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China;5.Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Shanghai 201306, China
Abstract:The Indian Ocean is an important marine fishery production area, and its marine catch has shown a continuously increasing trend in the past several decades. The scientific prediction of the catch trend in the future is of great significance to the development of China's pelagic fishery. Based on the catch data of Indian Ocean from 2000 to 2016, the main catch categories affecting the total catch were analyzed by using the grey system theory and method, and the catch of 2017 to 2018 were used for GM (1, N) models verification. Based on the optimal GM (1, N) , the total catch in the Indian Ocean from 2019 to 2025 was predicted. According to the study, the main categories that affect the total catch in the Indian Ocean were bottom fish, crustacean, pelagic fish, other marine fish and cephalopod, and their grey correlation degree is above 0.70. The optimal prediction model is GM (1, 5) and GM (1, 6) , the average relative error is 1.83% and 1.90% respectively, and the grey correlation degree is above 0.9. The average relative errors for 2017 and 2018 are 3.78% and 3.42% respectively. The total catch projections for 2019–2020 and 2021–2025 in the Indian Ocean are 11.86-12.90 million tons and 12.27-13.24 million tons, respectively. The main increases in catches are likely to come from pelagic, cephalopod and bottom fish. The study concluded that the growth of the total catch in the Indian Ocean during the 14th five-year plan period would limited, with a total increase of less than 0.8 million tons, and that the total catch be basically at the stage of full development. It is recommended that the scale of fisheries development should be strictly controlled in the future in order to ensure the sustainable development of marine fisheries and the sustainable utilization of fishery resources in the Indian Ocean.
Keywords:catch  grey correlation  GM (1   N) model  Indian Ocean
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