SVRNN:台湾海峡海温的时空预测模型

Haiqiang Chen;Yongxiang Chen;Zhenchang Zhang
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引用次数: 0

摘要

准确的海温预测在气候研究和海洋生态系统管理中具有重要作用。传统模型通过分析和拟合数据来预测趋势,但它们难以捕捉长期依赖关系和复杂的时空模式。转换器的注意机制有效地处理了远程依赖关系,但是它的高计算复杂性带来了挑战。为了克服这些局限性,本研究提出了一种新的时空序列预测模型:时空视觉曼巴递归神经网络(SVRNN)。该模型创新性地集成了双向状态空间处理机制和解耦存储模块。双向机制维持了一个具有线性计算复杂性的全局接受场,而解耦的记忆模块明确地分离了时空依赖性,增强了模型捕捉复杂时空模式的能力。在台湾海峡每小时海温预测实验中,SVRNN模型利用过去12 h的海温数据预测未来12 h的海温,结果表明,SVRNN模型的均方根误差(RMSE)为$0.159~^{\circ}$ C,平均绝对误差(MAE)为$0.105~^{\circ}$ C,平均绝对百分比误差(MAPE)为0.496%。此外,季节误差分析表明,模型在不同季节表现稳健,为台湾海峡海温预报提供了更可靠的技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SVRNN: A Spatiotemporal Prediction Model for Sea Surface Temperature Prediction in the Taiwan Strait
Accurate prediction of sea surface temperature (SST) plays a critical role in climate research and marine ecosystem management. Traditional models predict trends by analyzing and fitting data, but they struggle with capturing long-range dependencies and complex spatiotemporal patterns. The transformer’s attention mechanism effectively addresses long-range dependencies, but its high computational complexity poses challenges. To overcome these limitations, this study proposes a novel spatiotemporal sequence prediction model: the spatiotemporal vision mamba recurrent neural network (SVRNN). The model innovatively integrates a bidirectional state-space processing mechanism and decoupled memory modules. The bidirectional mechanism maintains a global receptive field with linear computational complexity, while the decoupled memory modules explicitly separate spatiotemporal dependencies, enhancing the model’s ability to capture complex spatiotemporal patterns. During the experiment on hourly SST prediction in the Taiwan Strait, where the SST of the next 12 h was predicted using data from the previous 12 h, the SVRNN model demonstrated superior performance, achieving a root mean square error (RMSE) of $0.159~^{\circ }$ C, a mean absolute error (MAE) of $0.105~^{\circ }$ C, and a mean absolute percentage error (MAPE) of 0.496%. Furthermore, our seasonal error analysis reveals that the model exhibits robust performance in different seasons, providing more reliable technical support for SST prediction in Taiwan Strait.
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