STA-SST: Spatio-temporal time series prediction of Moroccan Sea surface temperature

IF 2.1 4区 地球科学 Q2 MARINE & FRESHWATER BIOLOGY
Isam Elafi , Nabila Zrira , Assia Kamal-Idrissi , Haris Ahmad Khan , Aziz Ettouhami
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Abstract

Global Sea Surface Temperature (SST) trends have garnered significant attention in several ocean-related domains, including global warming, marine biodiversity, and environmental protection. This involves having an accurate and efficient forecast of future SST to ensure early detection and response in time to these events. Deep learning algorithms have become popular in SST prediction recently, although directly obtaining optimal prediction results from historical observation data is not simple. In this paper, we propose STA-SST, a new deep learning approach for forecasting SST, by combining the temporal dependencies of SST using the Bidirectional Long Short-Term Memory (BiLSTM) model, spatial features extracted from the convolution layer, and relevant information from the attention mechanism. To assess how well the Attention-BiLSTM with convolution layer predicts SST, we conducted a case study in the Moroccan Sea, concentrating on five different areas. The proposed model was compared against alternative forecasting models, including LSTM, XGBoost, Support Vector Regression (SVR), and Random Forest (RF). The experimental results show that STA-STT produces noticeably the best prediction results and is a solid choice for field implementation.

STA-SST:摩洛哥海面温度时空时间序列预测
全球海面温度(SST)趋势在多个与海洋相关的领域引起了极大关注,包括全球变暖、海洋生物多样性和环境保护。这就需要对未来的 SST 进行准确有效的预测,以确保及早发现并及时应对这些事件。虽然直接从历史观测数据中获取最优预测结果并不简单,但深度学习算法最近在 SST 预测领域大受欢迎。在本文中,我们利用双向长短期记忆(BiLSTM)模型将 SST 的时间依赖性、从卷积层提取的空间特征和注意力机制的相关信息结合起来,提出了预测 SST 的新型深度学习方法 STA-SST。为了评估带有卷积层的注意力-BiLSTM 预测 SST 的效果,我们在摩洛哥海进行了一项案例研究,主要集中在五个不同的区域。提出的模型与其他预测模型进行了比较,包括 LSTM、XGBoost、支持向量回归(SVR)和随机森林(RF)。实验结果表明,STA-STT 的预测结果明显最佳,是实地应用的可靠选择。
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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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