Fish Population Analysis by Neural Networks-Attempts for CPUE Prediction and Factorial Experiment-

• Shono Hiroshi

  National Research Institute of Far Seas Fisheries Fishery Rsearch Agency

• Tsubaki Hiroe

  Institute of Policy & Planning Science, University of Tsukuba

Bibliographic Information

Other Title

• ニューラルネットワークによる水産資源解析―ＣＰＵＥ予測と要因分析の試み―
• ニューラル ネットワーク ニ ヨル スイサン シゲン カイセキ CPUE ヨソク ト ヨウイン ブンセキ ノ ココロミ
• — Attempts for CPUE Prediction and Factorial Experiment —
• —CPUE予測と要因分析の試み—

Abstract

In the field of fish population dynamics, CPUE (catch per unit effort), defined as catch in number (or in weight) per fishing effort, is an important concept which is proportional to stock size. Because there are many areas without any operation in the fishery for southern bluefin tuna, the handling of such parts have a greatly effect on the estimated year trend of CPUE. Therefore, in this paper, we conducted the prediction of CPUE values in these missing cells using neural networks based on the spatial-temporal information. As a result of the reliability check by n-fold cross-validation, it shows rather high values of Pearson's correlation coefficient or low values of absolute error between observed CPUE and the corresponding predicted one based on neural networks. There values of correlation coefficient by neural networks are rather better than those by MCMC based on the multiple imputation method. We suggested the simple way for factorial experiment to extract year trends based on the estimated CPUE by neural networks. It was found that the extracted CPUE year trend based on this simple way using neural networks is rather similar to that by generalized linear models assuming that there exists some fish in the missing cells as well as surrounding areas. The results are consistent with so-called constant square hypothesis using generalize linear models in the CPUE analyses for southern bluefin tuna.

Journal

• Japanese Journal of Biometrics

  Japanese Journal of Biometrics 27 (1), 35-53, 2006

  The Biometric Society of Japan

Keywords

• catch per unit effort
• CPUE standardization
• factorial experiment
• MCMC
• neural networks
• n-fold cross-validation
• southern bluefin tuna

Details

• CRID: 1390282679347459840
• NII Article ID: 10018012966
• NII Book ID: AA11591618
• DOI: 10.5691/jjb.27.35
• ISSN: 21856494
• ISSN: 09184430
• NDL BIB ID: 8007725
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I8007725
• Text Lang: ja
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed