Time series prediction of sea surface temperature based on BiLSTM model with attention mechanism

IF 2.1 4区 地球科学 Q2 MARINE & FRESHWATER BIOLOGY
Nabila Zrira , Assia Kamal-Idrissi , Rahma Farssi , Haris Ahmad Khan
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Abstract

With the advancement of technology, ocean observation techniques have become increasingly prevalent in estimating marine variables such as Sea Surface Temperature (SST). This progress has led to a substantial surge in the volume of marine data. Presently, the abundance of available data presents a remarkable opportunity for training predictive models. The prediction of SST poses a challenge due to its temporal-dependent structure and multi-level seasonality. In this study, we propose a deep learning approach that combines the Bidirectional Long Short-Term Memory (BiLSTM) model with the attention mechanism to forecast SST. By leveraging the BiLSTM's ability to effectively capture long-term dependencies through both forward and backward LSTM processing, the attention mechanism accentuates salient features, thereby enhancing the model's evaluation accuracy.

To evaluate the effectiveness of the Attention-BiLSTM model in predicting SST, we conducted a case study in the Moroccan Sea, focusing on four distinct regions. We compared the performance of the Attention-BiLSTM model against alternative models such as LSTM, Attention-BiGRU, XGBoost, Random Forest (RF), Support Vector Regression (SVR), and Transformers in forecasting the SST time series.

The experimental results unequivocally demonstrate that the Attention-BiLSTM model achieves significantly superior prediction outcomes and is a good candidate for deployment in the field.

基于关注机制的 BiLSTM 模型的海面温度时间序列预测
随着技术的进步,海洋观测技术在估算海面温度等海洋变量方面越来越普遍。这一进步导致海洋数据量激增。目前,丰富的可用数据为训练预测模型提供了难得的机会。由于 SST 具有时间依赖性结构和多级季节性,因此对其进行预测是一项挑战。在本研究中,我们提出了一种深度学习方法,将双向长短期记忆(BiLSTM)模型与注意力机制相结合来预测 SST。通过利用 BiLSTM 通过前向和后向 LSTM 处理有效捕捉长期依赖关系的能力,注意力机制突出了突出特征,从而提高了模型的评估精度。为了评估注意力-BiLSTM 模型在预测 SST 方面的有效性,我们在摩洛哥海进行了一项案例研究,重点关注四个不同的区域。我们比较了 Attention-BiLSTM 模型与 LSTM、Attention-BiGRU、XGBoost、Random Forest (RF)、Support Vector Regression (SVR) 和 Transformers 等其他模型在预测 SST 时间序列方面的性能。
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来源期刊
Journal of Sea Research
Journal of Sea Research 地学-海洋学
CiteScore
3.20
自引率
5.00%
发文量
86
审稿时长
6-12 weeks
期刊介绍: The Journal of Sea Research is an international and multidisciplinary periodical on marine research, with an emphasis on the functioning of marine ecosystems in coastal and shelf seas, including intertidal, estuarine and brackish environments. As several subdisciplines add to this aim, manuscripts are welcome from the fields of marine biology, marine chemistry, marine sedimentology and physical oceanography, provided they add to the understanding of ecosystem processes.
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