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

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 .