Performance of a Rectangular-Shaped Surface Velocity Radar for River Velocity Measurements

Son, Geunsoo; Kim, Dongsu; Kim, Kyungdong; Roh, Youngsin

doi:10.1007/s12205-023-0272-0

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…As per the manufacturer's data sheet, SVR's measurement accuracy stands at ±1% of the measured value. This impressive accuracy range extends from 0.1 m/s to 33 m/s, making SVR 3D a versatile and reliable tool for a wide range of water velocity measurements [32,33].…”

Section: Surface Velocity Radar (Svr)mentioning

confidence: 99%

Comparative Assessment of Different Image Velocimetry Techniques for Measuring River Velocities Using Unmanned Aerial Vehicle Imagery

Wijaya,

Liu,

Suharyanto

et al. 2023

Water

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The accurate measurement of river velocity is essential due to its multifaceted significance. In response to this demand, remote measurement techniques have emerged, including large-scale particle image velocimetry (LSPIV), which can be implemented through cameras or unmanned aerial vehicles (UAVs). This study conducted water surface velocity measurements in the Xihu River, situated in Miaoli County, Taiwan. These measurements were subjected to analysis using five distinct algorithms (PIVlab, Fudaa-LSPIV, OpenPIV, KLT-IV, and STIV) and were compared with surface velocity radar (SVR) results. In the quest for identifying the optimal parameter configuration, it was found that an IA size of 32 pixels × 32 pixels, an image acquisition frequency of 12 frames per second (fps), and a pixel size of 20.5 mm/pixel consistently yielded the lowest values for mean error (ME) and root mean squared error (RMSE) in the performance of Fudaa-LSPIV. Among these algorithms, Fudaa-LSPIV consistently demonstrated the lowest mean error (ME) and root mean squared error (RMSE) values. Additionally, it exhibited the highest coefficient of determination (R2 = 0.8053). Subsequent investigations employing Fudaa-LSPIV delved into the impact of various water surface velocity calculation parameters. These experiments revealed that alterations in the size of the interrogation area (IA), image acquisition frequency, and pixel size significantly influenced water surface velocity. This parameter set was subsequently employed in an experiment exploring the incorporation of artificial particles in image velocimetry analysis. The results indicated that the introduction of artificial particles had a discernible impact on the calculation of surface water velocity. Inclusion of these artificial particles enhanced the capability of Fudaa-LSPIV to detect patterns on the water surface.

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Section: Surface Velocity Radar (Svr)mentioning

confidence: 99%

Comparative Assessment of Different Image Velocimetry Techniques for Measuring River Velocities Using Unmanned Aerial Vehicle Imagery

Wijaya,

Liu,

Suharyanto

et al. 2023

Water

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“…The V radar (RG-30 manufactured by Sommer) works on the Doppler principle. For more details about the radar Doppler technology, the readers can refer to the study presented by Fulton et al (2020), Son et al (2023) and Huang et al (2023). The V radar points slightly further downstream at an angle of 32 degrees to the vertical and with a total opening angle of 12°, so that the centre of the zone targeted by the V radar is located 3.7 m further downstream for very low flow conditions (respectively 2.5 m further downstreal for a water depth of 2 m).…”

Section: Site Description and Monitoring Equipmentmentioning

confidence: 99%

“…The literature frequently states that surface velocity radars do not perform well in the lowvelocity range (Welber et al, 2016;Fulton et al, 2020;Rahman Khan et al, 2021;Son et al, 2023), even though manufacturers claim that instrument sensitivity and resolution are good even at low velocities. In fact, the environmental conditions encountered for low velocities make measurements less reliable.…”

Section: Position Of the "Y Axis"mentioning

confidence: 99%

Streamflow monitoring at high temporal resolution based on non-contact instruments in a river prone to bathymetric shifts

Nord,

Safdar,

Hasanyar

et al. 2024

Preprint

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This study presents a reliable methodology for monitoring streamflow in a dynamic river of the Alps prone to bathymetric changes using non-contact instruments. The method relies on water level and surface velocity radar monitoring, discharge measurements by Large-Scale Particle Image Velocimetry (LSPIV), and topographic surveys. A single proportional relation, resistant to bathymetric changes, is established between maximum surface velocity (Vs,max) and bulk velocity (Umean). Different methods are used to build this relation: (i) an empirical approach calibrated with the LSPIV measurements; (ii) the Isovel model; (iii) the Q-Commander software developed by the Sommer company. The applicability of the method is tested over a 2.5-year dataset. Compared to the empirical approach, both models, which require minimal input data, predict well the Vs,max-Umean relation. The location of the maximum surface velocity, which reveals to be resistant to bathymetric changes, is also well predicted by these models. Discharge is calculated at a time step of 10 min by multiplying the bulk velocity and the wetted area. The results are compared to the discharge series at the historical station located 2.5 km further upstream, which has a stage-discharge rating curve. Good agreement is observed when surface velocity is above 0.7 m/s, but accuracy decreases for lower velocities. A simplified uncertainty analysis estimates a 20% relative error on discharge calculated with the presented method.

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A Review on the Video-Based River Discharge Measurement Technique

Chen,

et al. 2024

Sensors

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The hydrological monitoring of flow data is important for flood prevention and modern river management. However, traditional contact methods are increasingly struggling to meet the requirements of simplicity, accuracy, and continuity. The video-based river discharge measurement is a technique to monitor flow velocity without contacting the water body by using the image-recognition algorithms, which has been verified to have the advantages of full coverage and full automation compared with the traditional contact technique. In order to provide a timely summary of the available results and to inform further research and applications, this paper reviews and synthesizes the literature on the general implementation routes of the video-based river discharge measurement technique and the principles and advances of today’s popular image-recognition algorithms for velocity detection. Then, it discusses the challenges of image-recognition algorithms in terms of image acquisition conditions, parameter uncertainties, and complex meteorological and water environments. It is concluded that the performance of this technique can be improved by enhancing the robustness and accuracy of video-based discharge measurement algorithms, minimizing weather effects, and improving computational efficiency. Finally, future development directions for further perfecting this technique are outlined.

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Performance of a Rectangular-Shaped Surface Velocity Radar for River Velocity Measurements

Cited by 3 publications

References 19 publications

Comparative Assessment of Different Image Velocimetry Techniques for Measuring River Velocities Using Unmanned Aerial Vehicle Imagery

Comparative Assessment of Different Image Velocimetry Techniques for Measuring River Velocities Using Unmanned Aerial Vehicle Imagery

Streamflow monitoring at high temporal resolution based on non-contact instruments in a river prone to bathymetric shifts

A Review on the Video-Based River Discharge Measurement Technique

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