###### Abstract  
This paper proposes an algorithm based on the long short-term memory (LSTM) network to improve the quality of high-frequency surface wave radar current measurements. In order to address the limitations of traditional high-frequency radar inversion algorithms, which solely rely on electromagnetic inversion and disregard physical oceanography, this study incorporates a bottom-mounted acoustic Doppler current profiler (ADCP) and towed ADCP into LSTM training. Additionally, wind and tidal oceanography data were included as inputs. This study compared high-frequency radar current data before and after calibration. The results indicated that both towed and bottom-mounted ADCP enhanced the quality of HF radar monitoring data. By comparing two methods of calibrating radar, we found that less towed ADCP data input is required for the same high-frequency radar data calibration effect. Furthermore, towed ADCP has a significant advantage in calibrating high-frequency radar data due to its low cost and wide calibration range. However, as the location of the calibrated high-frequency radar data moves further away from the towing position, the calibration error also increases. This article conducted sensitivity studies on the times and different positions of using towed ADCP to calibrate high-frequency radar data, providing reference for the optimal towing path and towing time for future corrections of high-frequency radar data. P eurm  
control of marine pollution. Monitoring ocean current velocity data aids relevant departments in promptly detecting environmental issues, predicting and preventing potential marine disasters, planning pragmatic resource development and utilization programs, formulating scientifically grounded and effective pollution prevention and control measures, and advancing sustainable economic and social development in the Pearl River Estuary. Therefore, the significance of monitoring marine current velocity in the Pearl River Estuary is indisputable. High-frequency (HF) radar uses microwave signals generated with a transmitter to be sent out to sea, and the return signal is received by using a receiver. The interaction of the microwave signal with the sea current causes a Doppler shift phenomenon, resulting in a change in the frequency of the echo signal. By analyzing the Doppler shift of the echo signal, it is possible to calculate the speed and direction of the sea current [1; 2; 3; 4; 5]. High-frequency radar can therefore accurately monitor large areas of ocean surface dynamics, including currents, winds, and waves, under all weather conditions. This method has the advantages of being cost-effective, having a wide range, and providing strong real-time monitoring capabilities. However, during the observation process, radar-detected current data quality may be affected by ionospheric or transient interference, power outages, hardware or software failures, vandalism, or environmental constraints. Therefore, machine learning methods are necessary to ensure the completeness and accuracy of the data across the entire detection area. [PERSON] et al. [6] observed, compared, and evaluated sea surface currents on the West Florida Shelf using radar and Doppler tachometer systems. [PERSON] et al. [7] demonstrated the accuracy and reliability of a three-dimensional primitive equation model by successfully simulating semidiurnal currents in the waters of New York Bay and Block Island, validating the model with measured data and radar observation. [PERSON] et al. [8] validated the quality of high-frequency radar data obtained from the new radar stations in the Pearl River Estuary of China, discussed the impact of coastlines and open seas on the radar data quality, and proposed an improved machine learning inversion algorithm to enhance it. The ADCP (acoustic Doppler current profiler) is an instrument that utilizes the acoustic Doppler effect to measure the flow velocity of a body of water. It is widely used in the fields of oceanography, hydrography, and engineering.