基于CEEMDAN-LSTM风暴潮潮位预测分析研究

徐楚天; 沈良朵; 班文超; 陈亮

doi:10.19912/j.0254-0096.tynxb.2023-1132

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (11) : 578-585. DOI: 10.19912/j.0254-0096.tynxb.2023-1132

基于CEEMDAN-LSTM风暴潮潮位预测分析研究

徐楚天, 沈良朵, 班文超, 陈亮

作者信息 +

RESEARCH ON PREDICTION AND ANALYSIS OF STORM SURGE LEVEL BASED ON CEEMDAN-LSTM

Xu Chutian, Shen Liangduo, Ban Wenchao, Chen Liang

Author information +

文章历史 +

摘要

该文采用自适应噪声完全集合经验模态分解-长短期记忆方法（CEEMDAN-LSTM）对风暴潮潮位进行短期时间序列预测,并与常用机器学习模型进行对比分析,结果表明：基于CEEMDAN-LSTM的神经网络对工程区风暴潮潮位的短期特征能进行高精度的预报,其稳定性和精度较常规机器学习模型都有较大的改进。

Abstract

In this paper, an innovative approach is proposed, leveraging the complete ensemble empirical mode decomposition with adaptive noise and long short-term memory networks（CEEMDAN-LSTM） model, to forecast short-term time series of storm surge levels. The method is compared to commonly used machine learning models to assess its efficacy. The findings demonstrate that the CEEMDAN-LSTM neural network excels in accurately forecasting the short-term characteristics of storm surge levels within project areas. Notably, this model exhibits superior stability and precision compared to conventional machine learning models.

导出引用

徐楚天, 沈良朵, 班文超, 陈亮. 基于CEEMDAN-LSTM风暴潮潮位预测分析研究[J]. 太阳能学报. 2024, 45(11): 578-585 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1132

Xu Chutian, Shen Liangduo, Ban Wenchao, Chen Liang. RESEARCH ON PREDICTION AND ANALYSIS OF STORM SURGE LEVEL BASED ON CEEMDAN-LSTM[J]. Acta Energiae Solaris Sinica. 2024, 45(11): 578-585 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1132

中图分类号： P731.34

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