13 citations as recorded by crossref.

1. A hybrid model for significant wave height prediction based on an improved empirical wavelet transform decomposition and long-short term memory network J. Wang et al. 10.1016/j.ocemod.2024.102367
2. Recent Developments in Artificial Intelligence in Oceanography C. Dong et al. 10.34133/2022/9870950
3. Deep Learning-Based Enhanced ISAR-RID Imaging Method X. Wang et al. 10.3390/rs15215166
4. A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate G. Alkhayat et al. 10.3390/su152416759
5. Applying machine learning in devising a parsimonious ocean mixing parameterization scheme G. Han et al. 10.1016/j.dsr2.2022.105163
6. Prediction of significant wave height based on EEMD and deep learning T. Song et al. 10.3389/fmars.2023.1089357
7. Machine Learning for Simulation of Urban Heat Island Dynamics Based on Large-Scale Meteorological Conditions M. Varentsov et al. 10.3390/cli11100200
8. ASTMEN: an adaptive spatiotemporal and multi-element fusion network for ocean surface currents forecasting X. Li et al. 10.3389/fmars.2023.1281387
9. Significant wave height prediction based on deep learning in the South China Sea P. Hao et al. 10.3389/fmars.2022.1113788
10. Application of deep learning in estimating the convective mixing induced by brine rejection X. Gao et al. 10.1016/j.ocemod.2024.102314
11. Prediction of Electricity Generation Using Onshore Wind and Solar Energy in Germany M. Walczewski & H. Wöhrle 10.3390/en17040844
12. Assessing Long Short-Term Memory Network Significant Wave Height Forecast Efficacy in the Caribbean Sea and Atlantic Ocean B. Bethel et al. 10.2139/ssrn.4153300
13. A hybrid CEEMDAN-VMD-TimesNet model for significant wave height prediction in the South Sea of China T. Ding et al. 10.3389/fmars.2024.1375631