Acoustic doppler velocimeter backscatter for suspended sediment measurements: Effects of sediment size and attenuation

Şahin, Cihan; Öztürk, Mehmet; Aydoğan, Burak

doi:10.1016/j.apor.2019.101975

Cited by 10 publications

(9 citation statements)

References 34 publications

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“…In the last decade much work has been done to measure SPM concentration by optical/acoustic backscatter (Fugate and Friedrichs, 2002;Downing, 2006;Sahin et al, 2019). Commonly, the relationship between optical backscatter signal and SPM concentration was almost linear (Haalboom et al, 2021), and the acoustic backscatter signal was logarithmically proportional to the SPM concentration (Rouhnia et al, 2014;Öztürk, 2017;Sahin et al, 2017).…”

Section: Correlation Between Image and Optical/acoustic Backscatter S...mentioning

confidence: 99%

“…The true SPM concentration is calculated through gravimetric measurements of filtered and dried in-situ discrete water samples (Wren et al, 2000;Rai and Kumar, 2015). The optical and acoustic signals depend not only on the SPM concentration, but also on the inherent properties of SPM, such as the SPM size distribution and aggregation (Hatcher et al, 2001;Downing, 2006;Sahin et al, 2019), shape and composition (Ohnemus et al, 2018). Whereas, for in-situ long-period observation of deep-sea SPM, the inherent properties of SPM will change under complex hydrodynamic conditions.…”

Section: Introductionmentioning

confidence: 99%

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In-situ long-period monitoring of suspended particulate matter dynamics in deep sea with digital video images

Wang

Cong²,

Xia³

et al. 2022

Front. Mar. Sci.

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Suspended particulate matter (SPM) plays an important role in material transport, deposition, resuspension and the function of benthic communities’ processes in deep sea. SPM concentration data is usually indirectly measured by optical/acoustic sensors. However, converting these sensors’ signal to SPM concentration is associated with a number of uncertainties, which will lead to mis-estimation of the results. Some researchers recommend combining several optical/acoustic sensors to determine SPM concentration. However, due to the lack of corresponding video images, the interpretation of significant mismatch signals recorded by different sensors is subjective. Consequently, a better understanding of long-period SPM dynamics, especially in deep sea, is still a challenge. In this study, we seek to monitor the dynamics of SPM in deep sea, by firstly obtaining in-situ digital video images at a water depth of 1450 m on the northern slope of South China Sea in 2020, and secondly developing a digital image processing method to process the in-situ monitoring data. In this method, we defined an image signal which was the ratio between the area of the SPM and that of the total image, to characterize the SPM concentration. A linear regression model of the image signal and SPM concentration was established (R2 = 0.72). K-fold cross-validation showed that the performance of the model was well. We calculated the SPM concentration derived from image signal, and manually classified SPM into three distinct morphological groups. The long-period observation revealed that numerous aggregates existed in deep sea. The change of SPM concentration and morphology under hydrodynamics was synchronous. When current speed equaled to or exceeded 0.15 m/s, there was a significantly increase in SPM concentration and size. However, such increase was episodic. When current speed decreased, they will also decrease. In addition, we compared the image signal with the optical/acoustic backscattering signal, analyzed the mismatch period among these three signals. We found that the optical backscatter signal can’t accurately reflect the SPM concentration during the mismatch period. To our best knowledge, this is the first time that the in-situ digital video images were used to analyze the dynamics of SPM in deep sea.

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Section: Correlation Between Image and Optical/acoustic Backscatter S...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In-situ long-period monitoring of suspended particulate matter dynamics in deep sea with digital video images

Wang

Cong²,

Xia³

et al. 2022

Front. Mar. Sci.

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“…The classic method for turbidity measurement uses the Secchi disk [9]. Currently, the predominant methods for turbidity measurement hinge on physical principles, particularly the scattering of light [10,11] and sound [12,13]. Techniques such as nephelometry and turbidimetry quantify turbidity, expressed in nephelometric turbidity units (NTUs), by assessing the intensity of light scattered by suspended particles in water [14].…”

Section: Introductionmentioning

confidence: 99%

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

Parra,

Ahmad,

Sendra

et al. 2024

Chemosensors

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Turbidity is one of the crucial parameters of water quality. Even though many commercial devices, low-cost sensors, and remote sensing data can efficiently quantify turbidity, they are not valid tools for the classification it. In this paper, we design, calibrate, and test a novel optical low-cost sensor for turbidity quantification and classification. The sensor is based on an RGB light source and a light detector. The analyzed samples are characterized by turbidity values from 0.02 to 60 NTUs, and have four different sources. These samples were generated to represent natural turbidity sources and leaves in the marine areas close to agricultural lands. The data are gathered using 64 different combinations of light, generating complex matrix data. Machine learning models are compared to analyze this data, including training, validation, and test datasets. Moreover, different alternatives for data preprocessing and feature selection are assessed. Concerning the quantification of turbidity, the best results were obtained using averaged data and principal components analyses in conjunction with exponential gaussian process regression, achieving an R2 of 0.979. Regarding the classification of the turbidity, an accuracy of 91.23% is obtained with the fine K-Nearest-Neighbor classifier. The cases in which data were misclassified are characterized by turbidity values lower than 5 NTUs. The obtained results represent an improvement over the current solutions in terms of turbidity quantification and a completely novel approach to turbidity classification.

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“…To monitor TSS concentrations, a more confidential but powerful method, particularly due to the complexity of the signal analysis, is acoustic backscattering [ 11 , 12 ]. Such data can be obtained from different types of devices, from Acoustic Doppler Current Profilers (ADCPs), Acoustic Doppler Velocity Profilers (ADVPs) to simple Acoustic Doppler Velocimeters (ADVs) [ 13 ]. We used a commercial ADVP to record the acoustic backscattered amplitude, directly proportional to the acoustic turbidity.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Time Resolved Optical Turbidity Measurements for Water Monitoring to Standard Real-Time Techniques

Pallarès

Schmitt

Uhring

2021

Sensors

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Environmental water monitoring requires the estimation of the suspended solids load. In this paper, we compare the concentration range accessible through three different techniques: optical turbidity, acoustic backscattering and the newly in-lab developed time resolved optical turbidity. We focus on their comparison on measurements made in the laboratory on water suspensions of known particles and concentrations. We used laboratory grade kieselguhr, wheat starch and kaolin as suspended solid surrogates. The explored concentration domains are the ones, for the total suspended solid load, commonly encountered in wastewater and rivers in standard (less than 1 g/L to a few g/L) or extreme conditions such as floods or storm events (up to several dozen g/L). Regarding the operable concentration domain, the time resolved optical turbidity shows a clear advantage upon the other methods, whatever the kind of particle is.

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Acoustic doppler velocimeter backscatter for suspended sediment measurements: Effects of sediment size and attenuation

Cited by 10 publications

References 34 publications

In-situ long-period monitoring of suspended particulate matter dynamics in deep sea with digital video images

In-situ long-period monitoring of suspended particulate matter dynamics in deep sea with digital video images

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

Comparison of Time Resolved Optical Turbidity Measurements for Water Monitoring to Standard Real-Time Techniques

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