Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory
This paper investigates the possibility of using machine learning technology to correct wave height series numerical predictions. This is done by incorporating numerical predictions into long short-term memory (LSTM). Specifically, a novel ocean wave height series prediction framework, referred to a...
Main Authors: | Xiaoyu Zhang, Yongqing Li, Song Gao, Peng Ren |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-05-01
|
Series: | Journal of Marine Science and Engineering |
Subjects: | |
Online Access: | https://www.mdpi.com/2077-1312/9/5/514 |
Similar Items
-
Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network
by: Shuyi Zhou, et al.
Published: (2021-07-01) -
Time Series Forecasting of Significant Wave Height using GRU, CNN-GRU, and LSTM
by: Cornelius Stephanus Alfredo, et al.
Published: (2022-10-01) -
Prediction of Significant Wave Height in Offshore China Based on the Machine Learning Method
by: Zhijie Feng, et al.
Published: (2022-06-01) -
Significant wave height forecasting using WRF-CLSF model in Taiwan strait
by: Jinshan Ma, et al.
Published: (2021-01-01) -
PWPNet: A Deep Learning Framework for Real-Time Prediction of Significant Wave Height Distribution in a Port
by: Cui Xie, et al.
Published: (2022-09-01)