FORECASTING MONTHLY FISH LANDING IN EAST COAST PENINSULAR MALAYSIA USING SARIMA-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 is increasingly crucial due to the growing awareness that these resources are limited. Effective fish landing management plays a pivotal role in making informed decisions, particularly in preserving fish stocks for future food security. This paper aims to develop the best model for forecasting monthly fish landings in East Coast Peninsular Malaysia using the Box-Jenkins method and Artificial Neural Networks (ANN). The monthly dataset was divided into training data (January 2012 to December 2019) and testing data (January 2020 to December 2021). Employing the Box-Jenkins approach, the SARIMA model (1,1,0)(0,1,1)12 with minimum AIC and BIC values was identified as the most effective. By utilising lag variables from the SARIMA model as inputs for the ANN, we developed the SARIMA-ANN model. Model accuracy was assessed using the mean absolute percentage error. The findings showed that the integration of the SARIMA–ANN model enhances accuracy in forecasting the monthly fish landing performance in East Coast Peninsular Malaysia.
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