Abstract
Effective wave height prediction is crucial for ocean development, marine planning, and other ocean-related projects in coastal areas. A novel hybrid ARIMA-LSTM model is proposed, combining the strengths of Autoregressive Integrated Moving Average (ARIMA) in modeling linear relationships and Long Short-Term Memory (LSTM) in capturing non-linear components within time series data. Applied to Hangzhou Bay and Zhoushan Lianghengshan area data, the ARIMA-LSTM model outperforms traditional ARIMA, Support Vector Machine (SVM), LSTM, and Backpropagation (BP) neural network models across different stations, time periods, and typhoon scenarios. This innovative approach provides valuable technical support for accurate short-term effective wave height predictions.
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All relevant data generated and utilized in this study are available from the first author upon reasonable request.
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We want to thank all participants of the of the article. We thank the referees and editors for their constructive feedback regarding the initial version of the manuscript.
Funding
This study is financially supported by General Projects of Zhoushan Science and Technology Bureau and the National Natural Science Foundation of China (No. 52101330).
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Conceptualization, W.-C.B.; methodology, W.-C.B.; software, L.-D.S.; validation, W.-C.B.; formal analysis, W.-C.B.; investigation, C.-J.C.; resources, L.-D.S.; data curation, W.-C.B.; writing—original draft preparation, Y.B.; writing—review and editing, W.-C.B.; visualization, W.-C.B.; supervision, L.-D.S.; project administration, L.-D.S.; funding acquisition, L.-D.S. All authors read and agreed to the published version of the manuscript.
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Communicated by: H. Babaie
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Ban, W., Shen, L., Chen, J. et al. Short-term prediction of wave height based on a deep learning autoregressive integrated moving average model. Earth Sci Inform 16, 2251–2259 (2023). https://doi.org/10.1007/s12145-023-01023-6
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DOI: https://doi.org/10.1007/s12145-023-01023-6