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基于CEEMDAN-SO-BiLSTM模型的海冰面积预测

Qiao Guo¹, Haoyu Zhang¹, Yuhao Zhang¹

¹Zhongnan University of Economics and Law, Wuhan, Hubei, China.

|July 24, 2023

概括

这项研究引入了一种新的海冰区域预测模型,使用先进的信号分解和由蛇优化器优化的BiLSTM神经网络. 综合方法提高了气候和环境监测的预测准确性.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
气候建模气候模型

背景情况:

准确的海冰区域预测对于气候监测和海上作业至关重要.
传统的时间序列模型经常与海冰数据的复杂,非线性动态作斗争.
海冰数据中的噪音和非静止性对预测建模提出了重大挑战.

研究的目的:

开发和验证海冰区域时间序列的强有力的预测模型.
提高海冰预测的准确性和可靠性.
为了利用先进的信号处理和机器学习技术来提高环境预测.

主要方法:

用完整的集体实证模式分解与适应噪声 (CEEMDAN) 来进行数据分解和降噪.
一个双向长短期记忆 (BiLSTM) 神经网络,由蛇优化器 (SO) 优化,被用于模式智能预测.
重建了个别模式预测,以生成最终的海冰面积预测.

主要成果:

拟议的组合模型在测试组中表现出强大的预测性能.
关键的绩效指标包括RMSE为1.047,MAE为0.815,SMAPE为3.938. 这些指标包括:
CEEMDAN-BiLSTM-SO模型有效地捕获了海冰区域时间系列中的基本模式.

结论:

CEEMDAN的综合方法用于信号分解和SO优化的BiLSTM用于预测,在海冰区域预测方面取得了重大进展.
该模型能够处理非静止性和噪声的能力提高了其在现实环境监测场景中的适用性.
该方法为预测海冰动态提供了可靠的工具,有助于气候研究和运营规划.

关键词:

每日预测每日预测这就是为什么BiLSTM.在CEEMDAN,你会发现.所以,所以,所以,所以.海冰地区的海冰区域.

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