| 渔情预报技术及模型研究进展 |
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| 引用本文： | 陈新军,高峰,官文江,雷林,汪金涛.渔情预报技术及模型研究进展[J].水产学报,2013,37(8):1270-1280. |
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| 作者姓名： | 陈新军 高峰 官文江 雷林 汪金涛 |
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| 作者单位： | 上海海洋大学,上海海洋大学,上海海洋大学,上海海洋大学,上海海洋大学 |
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| 基金项目： | 国家高技术研究发展计划项目(2012AA092303)；教育部博士点基金(20093104110002)；曙光计划跟踪项目(08GG14) |
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| 摘 要： | 随着渔业资源的衰退和渔业生产成本的增加,渔业企业对于渔情预报的要求不断提高,渔情预报技术和模型的研究受到了越来越多地重视,已经成为渔场学研究的重点之一。渔情预报可分为关于资源状况的预报、关于时间的预报和关于空间的预报,各类预报对渔业生产和管理都具有重要的意义。本文结合国内外研究现状,简要概述了渔情预报的理论和方法,包括渔情预报相关的渔场学基础、数据模型和预报模型,重点介绍了基于统计和机器学习、人工智能方法的渔情预报模型,并对各种模型在渔情预报应用中的优势与缺陷进行了总结,最后针对渔情预报系统应用中存在的问题,对渔情预报研究提出了一些建议：建立专为渔业服务的海洋环境预报系统；进行长期和系统的渔业资源调查,并针对不同鱼种和海区对数据获取和处理方法进行标准化和规范化；借助随机模拟方法降低模型不确定性,提高预报精度。
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| 关 键 词： | 渔情预报 预报模型 统计学 机器学习 |
| 收稿时间： | 2012/8/29 0:00:00 |
| 修稿时间： | 2013/3/31 0:00:00 |
Review of fishery forecasting technology and its models |
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| GAO Feng,CHEN Xin-jun,GUAN Wen-jiang,LEI Lin and WANG Jin-tao.Review of fishery forecasting technology and its models[J].Journal of Fisheries of China,2013,37(8):1270-1280. |
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| Authors: | GAO Feng CHEN Xin-jun GUAN Wen-jiang LEI Lin and WANG Jin-tao |
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| Affiliation: | College of Marine Sciences,Shanghai Ocean University,College of Marine Sciences,Shanghai Ocean University,College of Marine Sciences,Shanghai Ocean University,College of Marine Sciences,Shanghai Ocean University,College of Marine Sciences,Shanghai Ocean University |
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| Abstract: | As the decline of fishery resources and the incense of fishery production costs the research of fishery forecasting technology and models has drawn more and more attention and became one of the emphases of fishery oceanography. Fishery forecasting on time, location and fishery resources are essential to fishery production and management. In this text, the theory and methods of fishing condition forecasting are summarized, including fishery oceanography, data models and prediction models related to this subject. Prediction models based on statistics methods and machine learning and artificial intelligence methods are emphasized, as well as the advantages and drawbacks of each kinds of model. Some research perspectives of fishing condition forecasting models are also proposed, i.e. developing ocean environments forecasting system; conducting systematic fishery resources survey of long standing and the standardization and normalization of fishery data acquisition and processing; reducing the uncertainty of prediction models with stochastic simulation methods and improving the prediction accuracy. |
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| Keywords: | fishery forecasting prediction models statistics machine learning |
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