PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2017 | 24 | Special Issue S2 |

Tytuł artykułu

Yellowfin tuna (Thunnusalbacares) fishing ground forecasting model based on Bayes classifier in the South China Sea

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
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

Słowa kluczowe

Wydawca

-

Rocznik

Tom

24

Opis fizyczny

p.140-146,fig.,ref.

Twórcy

autor
  • Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, China, 200090 Shanghai, China
autor
  • Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, China, 200090 Shanghai, China
  • College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
autor
  • Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, China, 200090 Shanghai, China
autor
  • South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510300, China

Bibliografia

  • 1. ZHANG Peng, YANG Li, ZHANG Xufeng, et al. 2010. The present status and prospect on exploitation of tuna and squid fishery resources in South China Sea. SOUTH CHINA FISHERIES SCIENCE, 6(1):68-74.
  • 2. MENG Xiaomeng, YE Zhenjiang, WANG Yingjun. 2007. Review on fishery and biology of yellowfin tuna (Thunnusalbacares). South China Fisheries Science, 3(4):74-80.
  • 3. JI Shijian, ZHOU Weifeng, CHENG Tianfei, et al. 2015. On the forecast and analysis of fishing grounds in the open South China Sea. Modern Fisheries Information, 2015(2):98-105.
  • 4. FENG Bo, LI Zhonglu, HOU Gang. 2014. Biology and distribution of thunnusobesus and thunnusalbacresin the South China Sea. OceanologiaetLimnologiaSinica, 2014(4):886-894.
  • 5. WANGZhongduo, GUO Yusong, YAN Yunrong, et al. 2012. Population genetics of tunas in South China Sea inferred from control regions. Journal of Fisheries of China, 36(2): 191-201.
  • 6. Zagaglia C R, Lorenzzetti J A, Stech J L. 2004. Remote sensing data and longline catches of yellowfin tuna (Thunnusalbacares) in the equatorial Atlantic. Remote Sensing of Environment, 93(1–2):267-281.
  • 7. Georgakarakos S, Koutsoubas D, Valavanis V. 2006. Time series analysis and forecasting techniques applied on loliginid and ommastrephid landings in Greek waters. Fisheries Research. 78(1):55-71.
  • 8. Zainuddin M, Saitoh K, Saitoh SI. 2008. Albacore (Thunnusalalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data. Fisheries Oceanography. 17(2):61-73.
  • 9. YANG Xiaoming, DAI Xiaojie, ZHU Guoping. 2012. Geostatistical analysis of spatial heterogeneity of yellowfin tuna (Thunnusalbacares) purse seine catch in the western Indian Ocean. ActaEcologicaSinica, 32(15): 4682-4690.
  • 10. CUIXuesen, TANG Fenghua, ZHANG Heng, et al. 2015. The Establishment of Northwest Pacific Ommastrephesbartramii Fishing Ground Forecasting Model Based on Naive Bayes Method. Periodical of Ocean University of China, 45(2):37-43.
  • 11. ZHOU Weifeng, FAN Wei, CUIXuesen, et al. 2012. Fishing ground forecasting of bigeye tuna in the Indian Ocean based on Bayesian probability model.Fisheries Information and Strategy, 27(3):214-218.
  • 12. CUI Xuesen, CHEN Xuedong, FAN Wei. 2007. Development of tuna fishing grounds prediction model and system. CHINESE HIGH TECHNOLOGY LETTERS. 17(1): 100-103.
  • 13. FAN Wei, CHEN Xuezhong, SHEN Xinqiang. 2006. Tuna fishing grounds prediction model based on Bayes probability. JOURNAL OF FISHERY SCIENCES OF CHINA. 13(3): 426-431.
  • 14. TIAN Siquan, CHEN Xinjun. 2010. Impacts of different calculating methods for nominal CPUE on CPUE standardization. Journal of Shanghai Ocean University, 19(2):240-245.
  • 15. WANG Guocai. 2010. Research and application of Naive Bayesian classifier[dissertation]. Chongqing JiaotongUniversity.
  • 16. ZHU Xingyu. 2011. SPSS multivariate statistical analysis method and its application. Beijing: Tsinghua University Press.
  • 17. ZHAO Yingshi. 2003. The principle and method of remote sensing application. Beijing: Science Press, 206-207.
  • 18. JI Shijian, ZHOU Weifeng, XU Hongyun, et al. A WebGIS application: Tuna fishing ground forecasting information service system for the open South China Sea, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 2016, pp. 3628-3631.
  • 19. Stech J L, Zagaglia C R, Lorenzzetti J A. 2004. Remote sensing data and longline catches of yellowfin tuna (Thunnusalbacares) in the equatorial Atlantic. Remote Sensing of Environment, 93(1-2):267-281.
  • 20. Lan, Kuo-Wei, Evans, Karen, et al. 2013. Effects of climate variability on the distribution and fishing conditions of yellowfin tuna (Thunnusalbacares) in the western Indian Ocean. Climatic Change, 119(1):63-77.
  • 21. WANG Shaoqin, XU Liuxiong, ZHU Guoping, et al. 2014. Spatial-temporal profiles of CPUE and relations to environmental factors for yellowfin tuna Thunnus albacores from purse-seine fishery in Western and Central Pacific Ocean. Journal of Dalian Fisheries University, 2014(3):303-308.
  • 22. He R, Ke C, Timothy M, et al. 2010. Mesoscale variations of sea surface temperature and ocean color patterns at the Mid‐Atlantic Bight shelfbreak. Geophysical Research Letters, 37(9): 493-533

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.agro-af5f51cf-b1c7-4a5d-ac53-f88bfe3bbdde
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.