Prediction of yellowfin tuna (Thunnus albacares Bonnaterre, 1788) catch trend in the southern waters of the country based on ARIMA and neural network (NN) models

Document Type : Research Paper

Authors

1 Offshore Fisheries Research Center, Iranian Fisheries Science Research Institute, Agricultural Research, Education and Extension Organization, Chabahar, Iran

2 Iranian Fisheries Science Research Institute, Agricultural Research, Education and Extension Organization, Tehran, Iran

10.22059/jfisheries.2023.360328.1390

Abstract

The aim of this study is to develop different models of aquatic forecasting and try to predict yellowfin tuna catch in the southern waters of the country with minimum possible errors. The average catch (Yi ± S. D) and logarithm of catch (LogYi ± S. D) for the years 1997 to 2021 were 35,378 ± 13,744 tons (95% confidence interval 21,634 - 49,129 tons) and 4.51 ± 0.18 tons (95% CI The confidence interval was 4.33-4.69 tons), respectively. According to the Mann-Kendall test, the average catch has increased significantly during the mentioned period (over the last two decades) (Z = 3.80, P < 0.05).  Different ARIMA combined prediction models (ARIMA, (p, d, q)) were tested based on the AIC index, and the ARIMA model (1, 0, 0) had the best fit with the change trend of yellowfin tuna in the southern waters of the country (AIC =- 24). The predict of yellowfin tuna catch results in the neural network (NN) models was show that feed forward neural networks (FFNN) have better performance than other models and with less error (MAE=0.02 and RMSE=0.03). Also, according to the results of ARIMA time series and neural network models, it can be concluded that feed forward neural networks simulate catch this species fish with higher accuracy than time series models. It seems, forecasting the trend of aquatic catch can be an important tool for fisheries managers and planners for better and sustainable management of aquatic resources and should be given more attention.

Keywords

Main Subjects

References

Acquah, H.D., 2010. Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of an asymmetric price relationship. Journal of Development and Agricultural Economics 2(1), 001-006.
Asgharieskoui, M., 2002. Application of neural network in time series forecasting. Iranian Economic Research Quarterly 12(1), 96-69. (In Persian)
Azadtalaitepe, N., Bahmanesh, J., Mantsari, M., Vardinjad, V., 2015. Comparison of time series and artificial neural network methods in forecasting reference evapotranspiration (case study: Urmia). Irrigation Science and Engineering 38(4), 85-76.
Benzer, S., Benzer, R., 2019. Alternative growth models in fisheries: Artificial Neural Network. Journal of Fisheries 7(3), 719-725. DOI: 10.17017/j.fish.137.
Box, G., Jenkins, G., 1976. Time Series Analysis, Forecasting and Control. Holden-Day, San Francisco, CA.  125 p.
Castellano-Mendez, M., Gonzalez- Manteiga, W., Febrero- Bande, M., Prada-Sanchez, J. M., R. Lozano-Calderon., 2004. Modelling of monthly and daily behavior of the run off the Xallas River using Box-Jenkins and neural networks methods. Journal of Hydrology 296(1), 38-58. DOI: 10.1016/j.jhydrol.2004.03.011
Chen, Y., Song, L., Liu, Y., Yang, L., Li, D., 2020. A Review of the Artificial Neural Network Models for Water Quality Prediction. Applied Sciences 10(5776), 2-49. DOI: 10.3390/app10175776.
Cook, S., Holly, S., Turner, P., 2003. The Power of Tests for Non-linearity: the case of Granger–Lee Asymmetry. Economics Letters 62(2), 155-159.
FAO., 2018. FAO Global Capture Production database updated to 2015 - Summary information.
FAO., 2022. The State of World Fisheries and Aquaculture 2022 - Meeting the sustainable development goals. Rome. Licenses: CC BY-NC-SA 3.0 IGO. 227 p.
Haruna, A., Mallawa, A., Musbir, M., Zainuddin, M., 2018. Population dynamic indicator of the Yellowfin tuna Thunnus albacares and its stock condition in the Banda Sea, Indonesia. AACL Bioflux 11(4), 1323-1333.
Hashemi, S.A.R., Doustdar, M., Gholampour, A., Khanehzaei, M., 2020. Length-based fishery status of yellowfin tuna (Thunnus albacares Bonnaterre, 1788) in the northern waters of the Oman Sea. Iranian Journal of Fisheries Sciences 19(6), 2790-2803. DOI: 10.22092/ijfs.2020.122747. (in Persian)
Hashemi, S.A.R., Mirzaei, M., 2019. Prediction of catch changes of Largehead hairtail (Trichiurus lepturus) fish in the coastal waters of the south of the country (Persian Gulf and Oman Sea). Journal of Aquatic Ecology 9(2), 140-149. (In Persian)
Iran Fisheries Organization(IFO), 2022. Statistical Yearbook of Iran Fisheries Organization. Iranian Fisheries Organization, Vice President of Planning and Management Development, Program and Budget Office. 65 p. (in Persian)
John, M.E., Reddy, K.S.N., 1989. Some considerations on the population dynamics of yellowfin tuna, Thunnus albacares (Bonnaterre) in Indian Seas. Studies on fish stock assessment in Indian waters. Foreign Service Institute Special Publication 2(1), 33-54.
Karmaker, C.L., Halder, P.K., Sarker, E., 2017. A Study of Time Series Model for Predicting Jute Yarn Demand: Case Study. Journal of Industrial Engineering 2017(1), 1-8. DOI: 10.1155/2017/2061260.
Kaschner, K., Kesner-Reyes, K., Garilao, C., Rius-Barile, J., Rees, T., Froese, R., 2016. AquaMaps: predicted range maps for aquatic species. World Wide Web electronic publication, www.aquamaps.org, Version 08/2016.
Kaymaram, F., Emadi, H., Kiabi, B., 2000. 2nd IOTC proceedings, Victoria, 23-27 September, Seychelles, 283-285.
Kaymaram, F., Hossini, A., Darvishi, M., 2014. Estimates of Length-Based Population Parameters of Yellowfin Tuna (Thunnus albacares) in the Oman Sea. Turkish Journal of Fisheries and Aquatic Sciences 14(1), 101-111. DOI: 10.4194/1303-2712-v14_1_12.
Koutroumanidis, T., Iliadis, L., Sylaios, G., 2006. Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software. Environmental Modelling and Software 21(1), 1711-1721. DOI: 10.1016/j.envsoft.2005.09.001
Lawer, E.A., 2016. Empirical Modeling of Annual Fishery Landings. Natural Resources 7(1), 193-204. DOI: 10.4236/nr.2016.74018
Martinez, F., Charte, F., Frías, M., Martínez-Rodríguez, M., 2022. Strategies for time series forecasting with generalized regression neural networks. Neurocomputing 491(1), 509-521. DOI: 10.1016/j.neucom.2021.12.028
Martinez, F., Frías, M., Perez, M.D., Rivera, A.J., 2022. Time Series Forecasting by Generalized Regression Neural Networks Trained with Multiple Series. IEEE Access 10(1), 1-8. DOI: 10.1109/ACCESS.2022.3140377
Michael Pawlus, M., Devine, R., 2020. A practical guide to designing, building, and improving neural network models using R. Packt Publishing. 317 p.
Nurdin, E., Sondita, M.F.A., Yusfiandayani, R., Baskoro, M.S., 2016. Growth and mortality parameters of yellowfin tuna (Thunnus albacares) in Palabuhanratu waters, west Java (eastern Indian Ocean). AACL Bioflux 9(3), 741-747.
Panhwar, S., Liu, Q., Khan, F., 2010. Selecting the Best Growth Model for Fish Using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Journal of the Fisheries Society of Taiwan 37(3), 183-190.
Prathibha, R., Syda Rao, G., Rammohan, K., 2012. Age, growth and population structure of the yellowfin tuna Thunnus albacares (Bonnaterre, 1788) exploited along the east coast of India. Indian Journal of Fisheries 59(1), 1-6.
R Development Core Team., 2022. R: A Language and Environment for Statistical Computing. Vienna: Foundation for Statistical Computing. http://www.R-project.org.
Ramalingam, A.B., Kar, L., Govindaraj, K., Prasad, G.V.A., 2012. Study of the growth and population parameters of yellowfin tuna (Thunnus albacares) in the Andaman and Nicobar waters based on the length frequency data. Report of14th IOTC proceedings. 24–29 October. Mauritius: 17 p.
Riede, K., 2004. Global register of migratory species - from global to regional scales. Final Report of the R&D-Projekt 808 05 081. Federal Agency for Nature Conservation, Bonn, Germany. 329 p.
Rodrigo Silvestre Martins, J.A., 2006. Cephalopods and fish attracted by night lights in coastal shallow-waters, off southern Brazil, with the description of squid and fish behavior. Revista de Etologia 4(1) 151-160.
Rosenberg, A.A., Bolster, W.J., Alexander, K.E., Leavenworth, W.B., Cooper, A.B., McKenzie, M.G., 2005. The history of ocean resources: modeling cod biomass using historical records. Frontiers in Ecology and the Environment 3(2), 84-90. DOI: 10.1890/1540-9295(2005)003[0078:THOORM]2.0.CO;2
Schultz, K., 2011. Ken Schultz's Field Guide to Saltwater Fish. New Jersey: John Wiley & Sons.230 p.
Shabri, A., Samsudin. R., 2015. Fishery Landing Forecasting Using Wavelet-Based Autoregressive Integrated Moving Average Models.  Hindawi Publishing Corporation Mathematical Problems in Engineering, Volume 2015, Article ID 969450, 9. DOI: 10.1155/2015/969450

Skaar, S., 2020. A Comprehensive Guide to Neural Network Modeling. Nova Science Pub Inc. 172 p.

Somvanshi, V.S., Bhargava, A.K., Gulati, D.K., Varghese, S., Varghese, S.P., 2003. Growth parameters estimated for yellowfin tuna occurring in the Indian EEZ. 6th IOTC proceedings. Victoria, 3-12 June, Seychelles, 191-193.
Sturesson, A., Weitz, N., Persson, A., 2018. SDG 14: Life Below Water. A Review of Research Needs. Technical annex to the Formas report Forskning för Agenda 2030: Översikt av forskningsbehov och vägar framåt. Stockholm Environment Institute, Stockholm.
Taghavimotlagh, S.A., 2010. Population dynamics and biology of Largehead hairtail (Trichiurus lepturus) fish in the Iranian coasts of the Persian Gulf and the Sea of Oman. National Fisheries Science Research Institute, 87 p. (In Persian)
Taghavimotlagh, S.A., 2018. Economic fishes of Persian Gulf and Sea of Oman and forecasting sustainable harvesting of their stocks. National Fisheries Science Research Institute. 668 p. (in Persian)
Tantivala, C., 2000. Some biological study of yellowfin tuna (Thunnus albacares) and bigeye tuna (Thunnus Obesus) in the eastern Indian Ocean. 2nd IOTC proceedings. Victoria, 23-27 September, Seychelles, pp. 436-440.
Tiumentsev, Y., Egorchev, M., 2019. Neural Network Modeling and Identification of Dynamical Systems. Academic Press. 324 p. DOI: 10.1016/C2017-0-02854-9.
Tsitsika, E., Maravelias, C., Haralabous, J., 2007. Modeling and forecasting pelagic fish production using univariate and multivariate ARIMA models. Fisheries Science 73(1), 979-988. DOI: 10.1111/j.1444-2906.2007. 01426.x
Journal of Fisheries
Volume 76, Issue 4
December 2023
Pages 579-591

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