Short-term prediction of wave height based on a deep learning autoregressive integrated moving average model

被引：2
|
作者：
Ban, Wenchao [1 ]
Shen, Liangduo [1 ]
Chen, Jiachao [1 ]
Yang, Bin [1 ]
机构：
[1] Zhejiang Ocean Univ, Sch Ocean Engn Equipment, Zhoushan 316000, Peoples R China
来源：
EARTH SCIENCE INFORMATICS | 2023年 / 16卷 / 3期
基金：
中国国家自然科学基金;
关键词：
ARIMA-LSTM; SVM; BP; Short-term wave height prediction; SUPPORT VECTOR MACHINE; OCEAN;
D O I：
10.1007/s12145-023-01023-6
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：2251 / 2259
页数：9
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