A training strategy for enhancing prediction accuracy of high magnitude oceanic environmental factors based on deep learning model

Abstract

Oceanic environmental factors have marked impacts on marine ecosystems and economic development. Variations in high-magnitude oceanic environmental factors often lead to marine disasters and ecological crises. Previous studies have demonstrated the success of deep learning in marine environmental prediction tasks; however, limited research has specifically targeted high-magnitude oceanic environmental factors. Here, we propose a deep-learning model training strategy that enhances the model's attention to high-magnitude oceanic environmental factors by incorporating an adaptive weight matrix, thereby improving prediction accuracy. We evaluated the proposed training strategy for two prediction tasks: time-series and physics-based predictions. Compared to the baseline model, the prediction error for high-magnitude oceanic environmental factors was reduced by up to 56.93 % using this training strategy, and the stability of the model performance in medium-to long-term forecasts was enhanced. In addition, the suitability of the strategy across diverse scenarios was examined by assessing its robustness and computational overhead. This study provides new insights for more accurate prediction of oceanic environmental factors.