###### Abstract  
An algorithm based on a long short-term memory (LSTM) network is proposed to reduce errors from high-frequency surface wave radar current measurements. In traditional inversion algorithms, the radar velocities are derived from electromagnetic echo signals, with no constraints imposed by physical oceanographic processes. In this study, sea surface winds and tides are included in the LSTM algorithm to improve radar data. These physical factors provide the LSTM network with more oceanic information by which to constrain and improve its training efficiency. The results show that the domain-averaged root-mean-square errors of the radar-derived velocities are reduced from 0.22 to 0.09 m/s for the whole radar observation area. The overall correlation coefficient increases from 0.37 to 0.88. To provide a practical strategy for future field work, we conduct a set of sensitivity experiments, showing that the LSTM network based on one single point can be applied to other data points within a sub-domain. HF radar; machine learning; LSTM; EOF ellipse Article  
A Machine Learning-Based Correction Method for High-Frequency Surface Wave Wave Radar Current Measurements [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], J. A Machine Learning-Based Correction Method for High-Frequency Surface Wave Radar Current Measurements. _Appl. Sci._**2022**, 12, 12980. [[https://doi.org/10.3390/app122412980](https://doi.org/10.3390/app122412980)]([https://doi.org/10.3390/app122412980](https://doi.org/10.3390/app122412980))  
Academic Editor: [PERSON]  
Received: 28 November 2022  
Accepted: 14 December 2022  
Published: 17 December 2022  
**Published: 17 December 2022**  
**Published: **Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Accepted: 14 December 2022  
Accepted: 17 December 2022  
Accepted: 14 December 2022  
the significant wave height of HF radar. Temporal convolutional network can estimate wave height from x-band radar data [10]. Convective neural networks and gated recurrent units have also achieved favorable results with respect to current prediction based on HF radar [11; 12]. [PERSON] et al. [13] applied BP to the interpolation method for a flow field mapped by HF radar. [PERSON] and [PERSON] [14] used a neural network to infer a directional ocean spectrum from a HF radar Doppler spectrum. [PERSON] et al. [15] used an artificial neural network to estimate wind speed by using the first-order peak energy of the backscattered HF radar Doppler spectrum. [PERSON] [16] used ML to measure HF radar wind speed. In addition, ML has been involved in radar sea clutter suppression, angle estimation, and target detection [17; 18; 19; 20; 21]. It proves that ML has been widely applied to radar observation. In traditional inversion algorithms, the HF radar current measurements are derived solely from electromagnetic echo signals, with no constraints imposed by physical oceanographic processes. The radar echoes are easily interfered with environmental noise or obstacles, such as radio broadcasting, trees, islands, and so on. All of these local interference factors will affect the quality of radar receiving echoes and the accuracy of the inversion algorithm, producing uncertain bias or errors for radar-derived currents. On the other hand, in terms of physical oceanography, the ocean currents in the PRE area are determined dynamically by winds, tides, and river discharges, as well as local topography and coastlines. Therefore, in this study, a LSTM network is used to correct the radar-derived current velocities near the PRE. Sea surface winds and tides are included in the LSTM algorithm to constrain its training process. These physical factors provide the LSTM network with ocean dynamical information and help the LSTM obtain more accurate training results. In this section, we clarify the purpose and significance of this research. In the next section, the data and methods used in this study are introduced, and the results are presented in Section 3.