DOI QR코드

DOI QR Code

Forecasting common mackerel auction price by artificial neural network in Busan Cooperative Fish Market before introducing TAC system in Korea

인공신경망을 활용한 고등어의 위판가격 변동 예측 -어획량 제한이 없었던 TAC제도 시행 이전의 경우-

  • 황강석 (국립수산과학원 자원관리과) ;
  • 최정화 (국립수산과학원 자원관리과) ;
  • 오택윤 (국립수산과학원 자원관리과)
  • Received : 2011.09.14
  • Accepted : 2011.12.17
  • Published : 2012.02.28

Abstract

Using artificial neural network (ANN) technique, auction prices for common mackerel were forecasted with the daily total sale and auction price data at the Busan Cooperative Fish Market before introducing Total Allowable Catch (TAC) system, when catch data had no limit in Korea. Virtual input data produced from actual data were used to improve the accuracy of prediction and the suitable neural network was induced for the prediction. We tested 35 networks to be retained 10, and found good performance network with regression ratio of 0.904 and determination coefficient of 0.695. There were significant variations between training and verification errors in this network. Ideally, it should require more training cases to avoid over-learning, which leads to improve performance and makes the results more reliable. And the precision of prediction was improved when environmental factors including physical and biological variables were added. This network for prediction of price and catch was considered to be applicable for other fishes.

Keywords

Prediction;Artificial neural network;Virtual variable;Auction price;Common mackerel

Acknowledgement

Supported by : 국립수산과학원

References

  1. Aoki, I. and T. Komatsu, 1992. Neuro-computing for Forecasting the Catch of Young Sardine. Bull. Japan. Soc. Fish. Oceanogr., 56 (2), 113-120.
  2. Aoki, I., T. Komatsu and K. Hwang, 1999. Prediction of response of zooplankton biomass to climatic and oceanic change. Ecological Modelling, 120 (2-3), 261-270. https://doi.org/10.1016/S0304-3800(99)00107-6
  3. Asoh, H., 1989. Mathematical Properties of Neural Networks. Jr. Japan. Soc. Artificial Intelligence, 4 (2), 128-133.
  4. Charef, A., S. Ohshimo, I. Aoki and N.A. Absi, 2010. Classification of fish schools based on evaluation of acoustic descriptor characteristics. Fish. Sci. 76 (1), 1-11. https://doi.org/10.1007/s12562-009-0186-x
  5. Czerwinski, I.A., J.C. Gutierrez-Estrada and J.A. Hernando-Casal (2007) Short-term forecasting of halibut CPUE: Linear and non-linear univariate approaches. Fish. Res. 86 (2-3), 120-128. https://doi.org/10.1016/j.fishres.2007.05.006
  6. Esmaeili, A. and M.H. Tarazkar, 2010. Prediction of shrimp growth using an artificial neural network and regression models. Aquacult Int. 19 (4), 705-713
  7. Fantin-Cruz, I., O. Pedrollo, C.C. Bonecker, D. Motta- Marques and S. Loverde-Oliveira, 2010. Zooplankton Density Prediction in a Flood Lake (Pantanal -Brazil) Using Artificial Neural Networks. Internat. Rev. Hydrobiol. 95 (4-5), 330-342.
  8. Hauser-Davis, R.A., T.F. Oliveira, A.M. Silveira, T.B. Silva and R.L. Ziolli, 2010. Case study: Comparing the use of nonlinear discriminating analysis and Artificial Neural Networks in the classification of three fish species: acaras (Geophagus brasiliensis), tilapias (Tilapia rendalli) and mullets (Mugil liza). Ecological Inforamtics 5 (6), 474-478. https://doi.org/10.1016/j.ecoinf.2010.08.002
  9. Hunabashi M., 1992. Introduction for neuro-computing, Ohmsha, Tokyo, pp. 152.
  10. Hwang, K. and I. Aoki, 1997. An approach to neurocomputing for the forecast of the catches of multiple species in the set net of Seishyo region, western Sagami Bay. Nippon Suisan Gakkaishi, 63 (2), 549 -556. https://doi.org/10.2331/suisan.63.549
  11. Hwang, K., I. Aoki, T. Komatsu, H. Ishizaki, I. Shibata, 1996, Forecasting for the catch of jack mackerel in the Komekami set net by a neural network. Bull. Japan. Soc. Fish. Oceanogr., 60 (2), 136-142.
  12. Jang J.-S.R., C.-T. Sun and E. Mizutani, 1997. Neurofuzzy and soft computing: a cmoputational approach to learning and machine intelligence. Prentice-Hall, New Jersey, pp. 614.
  13. Kim D. S., 1993. Theory and application of neural network. Hightech-info, Seoul, pp. 387.
  14. Lin C.T. and C.S.G. Lee, 1995. Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems. Prentice-Hall, New Jersey, pp. 797.
  15. Matsuba H., 1993. Information processing by neural system. Shokoutou, Tokyo, pp. 191.
  16. Robotham, H., P. Bosch, J.C. Gutiérrez-Estrada, J. Castillo and I. Pulido-Calvo, 2010. Acoustic identification of small pelagic fish species in Chile using support vector machines and neural networks. Fish. Res. 102 (1-2), 115-122. https://doi.org/10.1016/j.fishres.2009.10.015
  17. Simpson, P. K., 1990. Artificial Neural Systems, Pergamon Press, New York, pp. 209.
  18. Smith, M., 1996. Neural Networks for Statistical Modeling, Internationa Thompson computer press, Boston, pp. 235.
  19. Yanez E., F. Plaza, J.C. Gutierrez-Estrada, N. Rodrrguez, M.A. Barbieri, I. Pulido-Calvo and C. Borquez, 2010. Anchovy (Engraulis ringens) and sardine (Sardinops sagax) abundance forecast off northern Chile: A multivariate ecosystemic neural network approach. Progress in Oceanography 87 (1-4), 242 -250. https://doi.org/10.1016/j.pocean.2010.09.015
  20. Yoo S. and C. Zhang, 1993. Forecasting of hairtail (Trichiurus lepturus) landings in Korean waters by times series analysis. Bull. Kor. Fish. Soc., 26 (4), 363-368.

Cited by

  1. Quality Properties and Processing Optimization of Mackerel (Scomber japonicus) Sausage vol.42, pp.10, 2013, https://doi.org/10.3746/jkfn.2013.42.10.1656