On the Sea Surface Temperature Forecasting Problem with Deep Dilation-Erosion-Linear Models

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-04-26 DOI:10.1016/j.bdr.2024.100455
Ricardo de A. Araújo , Paulo S.G. de Mattos Neto , Nadia Nedjah , Sergio C.B. Soares
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Abstract

The sea surface temperature (SST) is considered an important measure for detecting changes in climate and marine ecosystems. So, its forecasting is essential for supporting governmental strategies to avoid side effects on the global population. In this paper, we analyze the SST time series and suggest that a combination between a linear component and a nonlinear component with long-term dependency can better represent it. Based on this assumption, we propose a deep neural network architecture with dilation-erosion-linear (DEL) processing units to deal with this particular kind of time series. An empirical analysis is performed in this work using three SST time series, where we explore three statistical measures. The experimental results demonstrate that the proposed model outperformed recent and classical literature forecasting techniques according to well-known performance metrics.

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论深层扩张-侵蚀-线性模型的海面温度预报问题
海面温度(SST)被认为是检测气候和海洋生态系统变化的重要指标。因此,对其进行预测对于支持政府避免对全球人口造成副作用的战略至关重要。在本文中,我们分析了 SST 时间序列,并提出线性分量和非线性分量之间的组合具有长期依赖性,可以更好地代表 SST。基于这一假设,我们提出了一种带有扩张-侵蚀-线性(DEL)处理单元的深度神经网络架构,以处理这种特殊的时间序列。在这项工作中,我们使用三个 SST 时间序列进行了实证分析,探索了三种统计量。实验结果表明,根据著名的性能指标,所提出的模型优于最新的经典文献预测技术。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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