###### Abstract
The instrument works by transmitting sound waves and measuring the change in echo frequency to determine the water flow rate and direction. Towed ADCPs have been developed over the past few years and are suitable for measurements in wide seas. They can effectively minimize the interference of carriers with the instrument. This type of ADCP shares characteristics with ROV robots; [PERSON] et al. [9] discuss this in their work. The optimal interpolation (OI) method was used to determine the most probable evolution of the velocity field based on the constraints provided by the ADCP observations and their error statistics. [PERSON] et al. [10] observed tidal spatial patterns and vorticity by using a shipborne acoustic Doppler current meter (ADCP) measurement. [PERSON] et al. [11] compared shipborne acoustic Doppler current meter measurements of the flow in the waterway between Scotland and the Hebrides with large-scale numerical modeling. [PERSON] et al. [12] used a method based on shipborne acoustic Doppler current meter (ADCP) data analysis to subtract the effect of tidal movements, obtain sub-tidal fields in marine surveys, and combine them with other current velocity observations to obtain the best description of tides and sub-tides. [PERSON] et al. [13] compared high-frequency radar data with ADCP at the mouth of the Pearl River and captured tidal features and flow field structures through error analysis. Artificial neural networks have become increasingly popular due to their immunity to subjective and objective factors such as time, space, and technology. They are capable of processing large amounts of data at high speeds and are fault- and error-tolerant. Neural networks have been utilized to predict wave heights in various studies [14; 15; 16; 17]. [PERSON] et al. [18] employed backpropagation neural networks to efficiently forecast long-term tidal levels using short-term measurements. [PERSON] et al. [17] utilized a neural network approach to predict the wave height and breaking water depth during wave breaking. [PERSON] et al. [19] accurately predicted tide levels, including tidal water levels under strong meteorological influences, using a backpropagation neural network (BPNN) approach through the concepts of iterative multi-step prediction and cycle analysis. [PERSON] et al. [20] employed a machine learning approach that combines a long short-term memory (LSTM) deep recurrent neural network model with an AdaBoost-integrated learning model to predict the daily mean sea surface temperatures in the short- and medium-term. The LSTM method was used by [PERSON] et al. [21] to quickly predict nearshore effective wave height with improved accuracy. Machine learning was utilized to predict short- and medium-term daily mean sea surface temperature, nearshore effective wave height, radar sea clutter suppression, angle estimation, and target monitoring [22; 23; 24; 25; 26]. [PERSON] et al. [27] used the Princeton Ocean Model to study the circulation features in the Pearl River Estuary and their responses to various factors, successfully simulating the seasonal distribution of salinity and hydrodynamic processes. [PERSON] et al. [28] conducted a survey of navigation and numerical simulation research on the Pearl River Estuary and adjacent waters, revealing the characteristics of circulation and salinity structure, as well as changes in ebb tidal current velocity. The purpose of this study was to use six HF radar stations established in the Pearl River Estuary, towed ADCP data from 7 June 2023 to 9 June 2023, and bottom-mounted ADCP data from 8 June 2023 to 29 June 2023, and introduce two physical factors, namely, the wind and tide, to control the training and correction process and build LSTM network models in order to compare the correction effect of towed ADCP on the radar. The correction effect of ocean current data was compared with the correction effect of bottom-mounted ADCP on radar data, and the optimal number of days for model application was obtained, which improved the quality and applicability of the radar data.