全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于随机森林的印度洋长鳍金枪鱼渔场预报

DOI: 10.3969/j.issn.0253-4193.2013.01.018, PP. 158-164

Keywords: 随机森林,长鳍金枪鱼,渔场预报,印度洋

Full-Text   Cite this paper   Add to My Lib

Abstract:

为了提高远洋渔场预报水平和满足渔业生产的需要,提出了一种基于随机森林建立印度洋长鳍金枪鱼(Thunnusalalunga)渔场预报模型的方法。选取2002-2009年各个月份印度洋5°×5°格点渔业环境和时空数据(包括海表温度、叶绿素a浓度、表温距平、叶绿素a浓度距平、海表温度梯度强度和海面高度异常等数据)作为预测变量,利用长鳍金枪鱼的CPUE(Catchperuniteffort,单位:尾/千钩数)的三分位点将渔区划分为高CPUE、中等CPUE和低CPUE三种类型,作为响应变量,对数据进行训练。结果表明,当随机森林中决策树达到100以上时,袋外数据OOB(out-of-bag)的分类误差率趋于平稳。将训练得到的随机森林用于2010年印度洋长鳍金枪鱼分月渔场的预测,其概率等值面图与实际生产的渔场分布进行叠加比较,显示高CPUE渔场概率分布与实际渔场的位置及范围变化情况符合。通过ROC(RelativeOperatingCharacteristic)分析,高CPUE、中等CPUE和低CPUE的AUC(AreaUnderROCCurve)分别达到0.847、0.743和0.803,表明预测精度较高。最后对中等CPUE渔区预测精度相对较低的原因进行了分析。

References

[1]  Laurs R M, Fielder P C, Montgomery D R. Albacore tuna catch distributions relative to environmental features observed from satellites[J]. Deep-Sea Research,1984,31(9): 1085-1099.
[2]  方宇,邹晓荣,张敏,等.东南太平洋智利竹筴鱼栖息地指数的比较研究[J].海洋渔业,2010,32(2):178-185.
[3]  牛明香,李显森,徐玉成.基于广义可加模型的时空和环境因子对东南太平洋智利竹筴鱼渔场的影响[J].应用生态学报,2004,21(4):1049-1055.
[4]  Pan R, Yang Q, Pan S J. Mining Competent Case Bases for Case-based Reasoning[J]. Artificial Intelligence,2007,171(16/17): 1039-1068.
[5]  Lucas P J F. Bayesian analysis,pattern analysis and data mining in health care[J]. Current Opinion in Critical Care Medicine,2004,10(5):399-403.
[6]  Tu J V. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes[J].J Clin Epidemiol, 1996,49(11):1225-1231.
[7]  Cutler D R, Edwards T C, Jr Beard, et al. Random forests for classification in ecology[J]. Ecology,2007,88(11): 2783-2792.
[8]  Ismail A I, Morrison E C,Burt B A, et al. Natural history of periodontal disease in adults: findings from the Tecumseh Periodontal Disease Study, 1959-87[J]. Journal of Dental Research,1990,69(2):430-435.
[9]  Pi Qingling, Hu Jianyu. Analysis of Sea surface temperature fronts in the Taiwan Strait and its adjacent area using an Advanced Edge Detection Method[J]. Science China Earth Science, 2010,53(7):1008-1016.
[10]  Breiman L, Friedman J H, Olshen R A, et al. Classification and Regression Trees [C]. Belmont (CA): Wadsworth International Group, 1984.
[11]  Tom F. An introduction to ROC analysis[J]. Pattern Recognition Letters,2006,27: 861-874.
[12]  Hosmer D W,Lemeshow S. Applied Logistic Regression. 2nd[M]. New York: John Wiley & Sons, Inc., 2000: 156-164.
[13]  Macskassy S, Provost F. Confidence Bands for ROC Curves: Methods and an Empirical Study [R]. Proc. 1st Workshop ROC Analysis in AI: ROCAI, 2004: 61-70.
[14]  Saito S. Studies on fishing of albacore, Thunnus alalunga(Bonnaterre),by experimental deep-sea tuna longline [EB/OL].http://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/21856/1/21(2)_P107-184.pdf.
[15]  Cutler D R, Edwards T C, Beard K H, et al. Random forests for classification in ecology[J]. Ecology,2007, 88(11): 2783-2792.
[16]  崔利锋,许柳雄.世界大洋性渔业概况[M].北京:海洋出版社,2011,4:3-4.
[17]  Childers J, Snyder S, Kohin S. Migration and behavior of juvenile North Pacific albacore (Thunnus alalunga)[J]. Fisheries Oceanography, 2011, 20(3): 157-173.
[18]  Zainuddin M, Saitoh K, Saitoh S. Albacore(Thunnus alalunga)fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data[J]. Fish Oceanogr,2008, 17(2):61-73.
[19]  苏奋振,周成虎,杜云艳,等. 海洋渔业资源地理信息系统应用的时空问题[J].应用生态学报,2003,14(9): 1569-1572.
[20]  叶施仁,史忠植.基于CBR的中心渔场预报[J].高技术通讯,2001,11(5):64-68.
[21]  冯波,陈新军,许柳雄.应用栖息地指数对印度洋大眼金枪鱼分布模式研究[J].水产学报, 2007, 31(6): 805-812.
[22]  Breiman L. Random forests[J]. Machine Learning, 2001,45: 5-32.
[23]  Breiman L. Manual On Setting Up, Using, And UnderstandingRandom Forests V3.1 [EB/OL].http://oz.berkeley.edu/users/breiman/Using_random_forests_V3.1.pdf
[24]  Arnold J B. A Multidimensional Scaling Study of Semantic Distance[J]. Journal of Experimental Psychology Monograph,1973, 90(2):349-372.
[25]  Segal M R. Machine Learning Benchmarks and Random Forest Regression. San Francisco: Technical Report, Center for Bioinformatics & Molecular Biostatistics, University of California, 2004.
[26]  Reka D, Michael P S, Jeffrey J, et al. Oceanographic investigation of the American Samoa albacore(Thunnus alalunga) habitat and longline fishing grounds[J]. Fish. Oceanogr. 2007,16(6): 555-572.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133