Forecasting Monthly Fish Landing in East Coast Peninsular Malaysia Using SARIMA and Artificial Neural Network
DOI:
https://doi.org/10.46754/jmsi.2023.12.002Keywords:
Marine Fish Landing, Autoregressive Seasonal, Autoregressive Integrated Moving Average, Multilayer Feed Forward Neural Network, Box-Jenkins Method, SARIMA modelAbstract
Marine resources management has become vital nowadays due to the increases of awareness of these resources becoming limited. With regards to fish landing, it has been a crucial for the managers to make better decision making particularly the amount of fish stock need to be kept for future food security since fish is one of the main source of protein supply to the country. Hence, the objective of this study is to predict the Monthly Fish Landing in East Coast Peninsular Malaysia using Box–Jenkins Autoregressive Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Networks (ANN). The data obtained were split into two sets with ratio of 80% and 20%, training data was set from January 2012 until December 2019 while testing data from January 2020 to December 2021. The SAS and MATLAB were used to analyse and obtaining the best model of prediction. Based on Box-Jenkin approach, the best model of SARIMA was observed in SARIMA (1,1,0)(0,1,1)12 with AIC and SBC reach the minimum value at AIC=20.67209 and SBC=20.77894. By taking lag variables of SARIMA model as the input for ANN, we then developed SARIMA-ANN model. The findings showed that the integration of SARIMA–ANN model proven to be a feasible option for more accurate prediction of monthly fish landing performance in East Coast Peninsular Malaysia.
References
Yaakob, O., & Chau, Q. P. (2005). Weather downtime and its effect on fishing operation in Peninsular Malaysia. Jurnal Teknologi, 42(A), 13-26. http://dx.doi.org/10.11113/jt.v42.730
Koutroumanidis, T., Iliadis, L., & Sylaios, G. K. (2006). Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software. Environmental Modelling & Software, 21(12), 1711-1721.
Shabri, A., & Samsudin, R. (2015). Fishery landing forecasting using wavelet-based autoregressive integrated moving average models. Mathematical Problems in Engineering, 2015(SI), 1-9. https://doi.org/10.1155/2015/969450
Begg, G. A., Friedland, K. D., & Pearce, J. B. (1999). Stock identification and its role in stock assessment and fisheries management: An overview. Fisheries Research, 43(1-3), 1-8. https://doi.org/10.1016/S0165-7836(99)00062-4
Teh, L. S. L., Zeller, D., Cabanban, A., Teh, L. C. L., & Sumaila, U. R. (2007). Seasonality and historic trends in the reef fisheries of Pulau Banggi, Sabah, Malaysia. Coral Reefs, 26(2), 251-263. http://dx.doi.org/10.1007/s00338-006-0182-x
Raman, R., Sathianandan, T. V., Sharma A. P., & Mohanty, B. P. (2017). Modelling and forecasting marine fish production in Odisha using seasonal ARIMA model. National Academy Science Letters, 40(6), 393–397. https://doi.org/10.1007/s40009-017-0581-2
Anuja, A., Yadav, V. K., Bharti, V. S., Kumar, N. R. (2017). Trends in marine fish production inTamil Nadu using regression and autoregressive integrated moving average (ARIMA) model. Journal of Applied and Natural Science, 9(2), 653–657. http://dx.doi.org/10.31018/jans.v9i2.1252
Farhan, J., & Ong, G. P. (2018). Forecasting seasonal container throughput at international ports using SARIMA models. Maritime Economics & Logistics, 20, 131-148.
Bako, H. Y., Rusiman, M. S., Kane, I. L., & Matias-Peralta, H. M. (2013). Predictive modeling of pelagic fish catch in Malaysia using seasonal ARIMA models. Agriculture, Forestry and Fisheries, 2(3), 136-140.
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transaction on Automatic Control, 19, 716–723. http://dx.doi.org/10.1109/TAC.1974.1100705
Otu, O. A., Osuji, G. A., Opara, J., Mbachu, H. I., & Iheagwara, A. I. (2014). Application of Sarima models in modelling and forecasting Nigeria’s inflation rates. American Journal of Applied Mathematics and Statistics, 2(1), 16-28.
Dongare, A. D., Kharde, R. R., & Kachare, A. D. (2012). Introduction to artificial neural network. International Journal of Engineering and Innovative Technology (IJEIT), 2(1), 189-194.
Haykin, S. S. (2009). Neural networks and learning machines. New York: Prentice Hall/Pearson.
Lewis, C. D. (1982). Industrial and business forecasting methods. London: Butterworth.
Makridakis, S., & Hibon, M. (2000). The M3-Competition: Results, conclusion and implications. International Journal of Forecasting, 16, 451-476.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Journal of Mathematical Sciences and Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
