NDLI: Application of support vector regression to CPUE analysis for southern bluefin tuna Thunnus maccoyii, and its comparison with conventional methods

Content Provider	Springer Nature Link
Author	Shono, Hiroshi
Copyright Year	2014
Abstract	This paper describes the catch per unit effort (CPUE) standardization using three models for data mining (support vector regression, neural network and tree regression model) and two conventional statistical methods (analysis of variance and generalized linear model) using the actual fishery data for southern bluefin tuna Thunnus maccoyii. Statistical performances of these five models were compared based on mean square error, mean absolute error and three correlation coefficients, which are measured by the difference between the observed and the corresponding predicted values. As a result, the performance of support vector regression is equivalent to (or better than) that of neural networks, and these two models are superior to the tree regression model, analysis of variance, and generalized linear model based on CPUE analyses of actual fishery data for southern bluefin tuna. We suggest a simple method for factorial analysis to extract the CPUE year trend based on the predicted values obtained from these data mining models. This method is expected to contribute markedly to reduce the difficulty of estimating the CPUE year trends by these models for data mining and should be applied to CPUE analyses because of its ease of use, general versatility and high performance .
Starting Page	879
Ending Page	886
Page Count	8
File Format	PDF
ISSN	09199268
Journal	Fisheries Science
Volume Number	80
Issue Number	5
e-ISSN	14442906
Language	English
Publisher	Springer Japan
Publisher Date	2014-07-25
Publisher Place	Tokyo
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	CPUE standardization Data mining method Factorial analysis Generalized linear model Neural network Support vector regression Southern bluefin tuna Tree regression model Fish & Wildlife Biology & Management Freshwater & Marine Ecology Food Science
Content Type	Text
Resource Type	Article
Subject	Aquatic Science

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