Comment on papers using machine learning for significant wave height time series prediction: Complex models do not outperform auto-regression-Reference-Cited by-同舟云学术

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Comment on papers using machine learning for significant wave height time series prediction: Complex models do not outperform auto-regression

Published:2024-06 Issue: Volume:189 Page:102364
ISSN:1463-5003
Container-title:Ocean Modelling
language:en
Short-container-title:Ocean Modelling

Author:

Jiang Haoyu^ORCID,Zhang Yuan,Qian Chengcheng,Wang Xuan

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

National Key Research and Development Program of China

China University of Geosciences

Publisher

Elsevier BV

Reference28 articles.

1. A global wave parameter database for geophysical applications. Part 3: improved forcing and spectral resolution;Alday;Ocean Model.,2021

2. Hourly significant wave height prediction via singular spectrum analysis and wavelet transform based models;Altunkaynak;Ocean Eng.,2023

3. The “Swan” wave model for shallow water;Booij;Coast. Eng.,1996

4. Improving prediction performance of significant wave height via hybrid SVD-Fuzzy model;Çelik;Ocean Eng.,2022

5. Significant wave height prediction through artificial intelligent mode decomposition for wave energy management;Chen;Energy AI,2023

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Gradient Boosted Trees and Denoising Autoencoder to Correct Numerical Wave Forecasts;Journal of Marine Science and Engineering;2024-09-06

2. Prediction of significant wave height using a VMD-LSTM-rolling model in the South Sea of China;Frontiers in Marine Science;2024-06-19

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