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

Xu Chutian; Shen Liangduo; Ban Wenchao; Chen Liang

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

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (11) : 578-585. DOI: 10.19912/j.0254-0096.tynxb.2023-1132

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

Xu Chutian, Shen Liangduo, Ban Wenchao, Chen Liang

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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.

Key words

storm surge / offshore wind power / tide level prediction / complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN） / long short-term memory networks （LSTM）

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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

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