Designing Theoretical Shipborne ADCP Survey Trajectories for High-Frequency Radar Based on a Machine Learning Neural Network

Zhu, Langfeng; Yang, Fan; Yang, Yufan; Xiong, Z.; Wei, Jiangong

doi:10.3390/app13127208

Cited by 2 publications

(3 citation statements)

References 46 publications

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“…The capabilities of OSMAR for ocean current observations have been extensively validated and are consistent with the CODAR SeaSonde results [3,[32][33][34][35][36]. To address the high cost and space requirements of this radar system, a compact high-frequency radar system carrying two loops and a monopole, known as the OSMAR-S series, was developed [37].…”

Section: High-frequency Radar: Osmar-s100mentioning

confidence: 63%

Performance Assessment of a High-Frequency Radar Network for Detecting Surface Currents in the Pearl River Estuary

Zhu,

Lu,

Yang

et al. 2024

Remote Sensing

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The performance of a high-frequency (HF) radar network situated within the Pearl River Estuary from 17 July to 13 August 2022 is described via a comparison with seven acoustic Doppler current profilers (ADCPs). The radar network consists of six OSMAR-S100 compact HF radars, with a transmitting frequency of 13–16 MHz and a direction-finding technique. Both the radial currents and vector velocities showed good agreement with the ADCP results (coefficient of determination r2: 0.42–0.78; RMS difference of radials: 11–21.6 cm s−1; bearing offset Δθ: −4.8°–16.1°; complex correlation coefficient γ: 0.62–0.96; and phase angle α: −24.3°–17.8°). For these radars, the Δθ values are not constant but vary with azimuthal angles. The relative positions between the HF radar and ADCPs, as well as factors such as the presence of island terrain obstructing the signal, significantly influence the errors. The results of spectral analysis also demonstrate a high level of consistency and the capability of HF radar to capture diurnal and semidiurnal tidal frequencies. The tidal characteristics and the Empirical Orthogonal Function (EOF) results measured by the HF radars also resemble the ADCPs and align with the characteristics of the estuarine current field.

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Section: High-frequency Radar: Osmar-s100mentioning

confidence: 63%

Performance Assessment of a High-Frequency Radar Network for Detecting Surface Currents in the Pearl River Estuary

Zhu,

Lu,

Yang

et al. 2024

Remote Sensing

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“…In the prediction process, their respective parameters were used to validate the correction effect on radar data. Previous experiments conducted by Zhu et al [31,32] have shown that the model's correction of radar data is a function of time. Therefore, this study also conducted sensitivity experiments on the time of the input data in order to obtain the optimal tow route and towing time for correcting radar data.…”

Section: Lstm Neural Networkmentioning

confidence: 99%

“…In our previous study, we concluded that adding wind and tide to the input term can improve the training of the LSTM model. However, at that time, we used the flow velocity of the Finite-Volume Coastal Ocean Model (FVCOM) as the true value [32]. Therefore, as the closest approximation to the true ocean current data, obtained from ADCP measurements, we are curious as to whether incorporating wind and tidal factors can improve the correlation between the predicted values and the ADCP data.…”

Section: Experimentation With Inputsmentioning

confidence: 99%

High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration

Xiong,

Wei,

Yang

et al. 2024

Applied Sciences

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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.

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Designing Theoretical Shipborne ADCP Survey Trajectories for High-Frequency Radar Based on a Machine Learning Neural Network

Cited by 2 publications

References 46 publications

Performance Assessment of a High-Frequency Radar Network for Detecting Surface Currents in the Pearl River Estuary

Performance Assessment of a High-Frequency Radar Network for Detecting Surface Currents in the Pearl River Estuary

High-Frequency Surface Wave Radar Current Measurement Corrections via Machine Learning and Towed Acoustic Doppler Current Profiler Integration

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