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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

An Integrated Complete Ensemble Empirical Mode Decomposition with Adaptive Noise to Optimize LSTM for Significant Wave Height Forecasting
Lingxiao Zhao, Zhiyang Li, Junsheng Zhang, et al.
Journal of Marine Science and Engineering (2023) Vol. 11, Iss. 2, pp. 435-435
Open Access | Times Cited: 13

Showing 13 citing articles:

Wave energy forecasting: A state-of-the-art survey and a comprehensive evaluation
Ruobin Gao, Xiaocai Zhang, Maohan Liang, et al.
Applied Soft Computing (2025) Vol. 170, pp. 112652-112652
Closed Access | Times Cited: 1
Assessment and prediction of significant wave height using hybrid CNN-BiLSTM deep learning model for sustainable wave energy in Australia
Nawin Raj, Reema Prakash
Sustainable Horizons (2024) Vol. 11, pp. 100098-100098
Open Access | Times Cited: 7
A novel machine learning-based artificial intelligence method for predicting the air pollution index PM2.5
Lingxiao Zhao, Zhiyang Li, Leilei Qu
Journal of Cleaner Production (2024) Vol. 468, pp. 143042-143042
Closed Access | Times Cited: 7
Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns
Huimin Han, Harold Neira-Molina, Asad Khan, et al.
Journal of Cloud Computing Advances Systems and Applications (2024) Vol. 13, Iss. 1
Open Access | Times Cited: 6
Study on Noise Reduction of Hydrostatic Leveling Signals for Wind Turbine Foundations Based on CEEMDAN‐SG Algorithm
Li Ren-Jie, Xiangxing Lu, Zhixin Song, et al.
Advances in Civil Engineering (2025) Vol. 2025, Iss. 1
Open Access
Artificial intelligence-based forecasting models for integrated energy system management planning: An exploration of the prospects for South Africa
Senthil Krishnamurthy, Oludamilare Bode Adewuyi, Emmanuel Luwaca, et al.
Energy Conversion and Management X (2024) Vol. 24, pp. 100772-100772
Open Access | Times Cited: 3
A hybrid model for multistep-ahead significant wave height prediction using an innovative decomposition–reconstruction framework and E-GRU
Mie Wang, Feixiang Ying
Applied Ocean Research (2023) Vol. 140, pp. 103752-103752
Closed Access | Times Cited: 9
A hybrid model for significant wave height prediction based on an improved empirical wavelet transform decomposition and long-short term memory network
Jin Wang, Brandon J. Bethel, Wenhong Xie, et al.
Ocean Modelling (2024) Vol. 189, pp. 102367-102367
Closed Access | Times Cited: 3
A novel cryptocurrency price time series hybrid prediction model via machine learning with MATLAB/Simulink
Lingxiao Zhao, Zhiyang Li, Eric Yue, et al.
The Journal of Supercomputing (2023) Vol. 79, Iss. 14, pp. 15358-15389
Closed Access | Times Cited: 8
Hybrid intelligent models for predicting weekly mean significant wave heights
Dayong Han, Xinhua Xue
Ocean Engineering (2024) Vol. 310, pp. 118706-118706
Closed Access | Times Cited: 1
Novel Ocean Wave Height and Energy Spectrum Forecasting Approaches: An Application of Semi-Analytical and Machine Learning Models
Ismail Elkhrachy, Ali Alhamami, Saleh H. Alyami, et al.
Water (2023) Vol. 15, Iss. 18, pp. 3254-3254
Open Access | Times Cited: 4
Integration of the Non-linear Time Series GARCH Model with Fuzzy Model Optimized with Water Cycle Algorithm for River Streamflow Forecasting
Mohammad Amin Karami, Saeid Shabanlou, Hosein Mazaheri, et al.
International Journal of Computational Intelligence Systems (2024) Vol. 17, Iss. 1
Open Access
Wave predictor models for medium and long term based on dual attention-enhanced Transformer
Lina Wang, xulei wang, Changming Dong, et al.
Ocean Engineering (2024) Vol. 310, pp. 118761-118761
Closed Access
What works better with LSTM, decomposition or deseasonalisation for rainfall forecasting?
Achal Lama, Debopam Rakshit, Kehar Singh, et al.
Research Square (Research Square) (2024)
Open Access
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