<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Using the yellowfin tuna (Thunnusalbacares,YFT)longline fishing catch data in the open South China Sea (SCS) provided by WCPFC, the optimum interpolation sea surface temperature (OISST) from CPC/NOAA and multi-satellites altimetric monthly averaged product sea surface height (SSH) released by CNES, eight alternative options based on Bayes classifier were made in this paper according to different strategies on the choice of environment factors and the levels of fishing zones to classify the YFT fishing ground in the open SCS. The classification results were compared with the actual ones for validation and analyzed to know how different plans impact on classification results and precision. The results of validation showed that the precision of the eight options were 71.4%, 75%, 70.8%, 74.4%, 66.7%, 68.5%, 57.7% and 63.7% in sequence, the first to sixth among them above 65% would meet the practical application needs basically. The alternatives which use SST and SSH simultaneously as the environmental factors have higher precision than which only use single SST environmental factor, and the consideration of adding SSH can improve the model precision to a certain extent. The options which use CPUE’s mean ± standard deviation as threshold have higher precision than which use CPUE’s 33.3%-quantile and 66.7%-quantile as the threshold</p></abstract>

Using the yellowfin tuna (Thunnusalbacares,YFT)longline fishing catch data in the open South China Sea (SCS) provided by WCPFC, the optimum interpolation sea surface temperature (OISST) from CPC/NOAA and multi-satellites altimetric monthly averaged product sea surface height (SSH) released by CNES, eight alternative options based on Bayes classifier were made in this paper according to different strategies on the choice of environment factors and the levels of fishing zones to classify the YFT fishing ground in the open SCS. The classification results were compared with the actual ones for validation and analyzed to know how different plans impact on classification results and precision. The results of validation showed that the precision of the eight options were 71.4%, 75%, 70.8%, 74.4%, 66.7%, 68.5%, 57.7% and 63.7% in sequence, the first to sixth among them above 65% would meet the practical application needs basically. The alternatives which use SST and SSH simultaneously as the environmental factors have higher precision than which only use single SST environmental factor, and the consideration of adding SSH can improve the model precision to a certain extent. The options which use CPUE’s mean ± standard deviation as threshold have higher precision than which use CPUE’s 33.3%-quantile and 66.7%-quantile as the threshold

Yellowfin Tuna (Thunnusalbacares) Fishing Ground Forecasting Model Based On Bayes Classifier In The South China Sea

Polish Maritime Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Using the yellowfin tuna (Thunnusalbacares,YFT)longline fishing catch data in the open South China Sea (SCS) provided by WCPFC, the optimum interpolation sea...

Yellowfin Tuna (Thunnusalbacares) Fishing Ground Forecasting Model Based On Bayes Classifier In The South China Sea

Online veröffentlicht: 13. Sept. 2017

Seitenbereich: 140 - 146

DOI: https://doi.org/10.1515/pomr-2017-0076

Schlüsselwörter
Bayes classifier, South China Sea,yellowfin tuna, fishing ground forecasting

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Yellowfin Tuna (Thunnusalbacares) Fishing Ground Forecasting Model Based On Bayes Classifier In The South China Sea

Online veröffentlicht: 13. Sept. 2017

Seitenbereich: 140 - 146

DOI: https://doi.org/10.1515/pomr-2017-0076

SchlüsselwörterBayes classifier, South China Sea,yellowfin tuna, fishing ground forecasting

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Bayes classifier, South China Sea,yellowfin tuna, fishing ground forecasting