Predicting Total Fish Production Using An LSTM-Based Linear Attention Model
Abstract
Accurate prediction of fish production is essential for supporting food security, science-based policymaking, and catch quota regulation in Indonesia. However, reliable forecasting remains challenging due to inconsistencies in fisheries logbook data and the difficulty of capturing complex temporal patterns in production records. This study proposes a deep learning-based predictive framework for total fish production forecasting to provide timely scientific evidence for sustainable fisheries resource management. The proposed model employs a Trapezoid Long Short-Term Memory Linear Attention (LLA) network enhanced with a linear attention mechanism to capture temporal dependencies and assign adaptive weights to relevant feature-specific inputs. The annual total production variable was normalized using a global maximum value to ensure stable model training and comparable prediction outputs. Experimental evaluation showed promising predictive performance, with a root mean squared error (RMSE) of 0.00465 and a mean absolute error (MAE) of 0.00244 on the normalized scale. The model was trained by minimizing the mean squared error (MSE) loss function. The results indicate that the proposed framework can effectively approximate observed production trends and may serve as a precautionary decision-support tool for monitoring stock abundance indicators, improving catch quota planning, and promoting rational fisheries development in line with the sustainability of fishery resources.
Keywords: fish production prediction; deep learning; LSTM linear attention; sustainable fisheries management; food security; Indonesia; catch quota policy.
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