| 基于提升回归树的东、黄海鲐鱼渔场预报 |
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| 引用本文： | 高峰,陈新军,官文江,李纲.基于提升回归树的东、黄海鲐鱼渔场预报[J].海洋学报,2015,37(10):39-48. |
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| 作者姓名： | 高峰 陈新军 官文江 李纲 |
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| 作者单位： | 上海海洋大学 海洋科学学院, 上海 201306;上海海洋大学 大洋渔业资源可持续开发省部共建教育部重点实验室, 上海 201306;上海海洋大学 国家远洋渔业工程技术研究中心, 上海 201306;远洋渔业协同创新中心, 上海 201306 |
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| 基金项目： | 国家863项目(2012AA092301);国家发改委产业化专项(2159999);国家科技支撑计划(2013BAD13B01);上海市教委科研创新项目(14ZZ147)。 |
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| 摘 要： | 为提高东、黄海鲐鱼渔场预报准确率、降低渔业生产成本,研究提出了一种基于提升回归树的渔场预报模型。研究采用2003—2010年我国大型灯光围网渔捞日志数据,以有网次记录的小渔区为渔场,以渔捞日志未记录的区域作为背景场随机选择假定非渔场数据,以海表水温等环境因子作为预测变量构建东、黄海鲐鱼渔场预报模型并以2011年的实际作业记录对预报模型进行精度验证。验证计算得到预报模型的AUC(area under receiver operating curve)值为0.897,表明模型的预报精度较高。模型的空间预测结果表明,预报渔场与实际作业位置基本吻合,其位置移动也与实际情况相符。这表明基于提升回归树的渔场预报模型可以用来进行东、黄海鲐鱼渔场的预报。
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| 关 键 词： | 提升回归树 鲐鱼 渔场预报 东、黄海 |
| 收稿时间： | 2014/12/16 0:00:00 |
Fishing ground forecasting of chub mackerel in the Yellow Sea and East China Sea using boosted regression trees |
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| Gao Feng,Chen Xinjun,Guan Wenjiang and Li Gang.Fishing ground forecasting of chub mackerel in the Yellow Sea and East China Sea using boosted regression trees[J].Acta Oceanologica Sinica (in Chinese),2015,37(10):39-48. |
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| Authors: | Gao Feng Chen Xinjun Guan Wenjiang and Li Gang |
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| Institution: | College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China;National Distance-water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China;Collaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China |
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| Abstract: | To improve the accuracy of fishing ground forecasting of chub mackerel (Scomber japonicus) in the Yellow and East China Sea,and reduce the fishery production cost,a new fishing ground forecasting model based on boosted regression trees was proposed in this study. Model was fitted with data extracted from electronic logbooks of Chinese mainland large-type lighting purse seine fishery for chub mackerel,with a range from 2003 to 2010. The fishing area with fishing effort was identified as fishing ground and the pseudo non fishing ground data was randomly collected from background field,which is the fishing areas with no records in the logbooks. The predictive variables were sea surface temperature and other environmental factors. The performance of prediction of the model was evaluated with the testing dataset consist of actual fishing locations of year 2011. The results of the evaluation showed that the prediction model had a high prediction performance with an AUC value of 0.897. The results of spatial prediction showed that the predicted fishing ground and its shifting were coincided with the actual fishing locations,which indicated that the forecasting model based on boosted regression trees can be used to forecasting the fishing ground of chub mackerel in the Yellow and East China Sea. |
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| Keywords: | boosted regression trees chub mackerel fishing ground forecasting Yellow Sea East China Sea |
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