Bedload transport analysis using image processing techniques

Ermilov, Alexander Anatol; Fleit, Gábor; Conevski, Slaven; Guerrero, Massimo; Baranya, Sándor; Rüther, Nils

doi:10.1007/s11600-022-00791-x

Cited by 11 publications

(6 citation statements)

References 52 publications

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“…It is possibly because only 3D CFD modeling is adequate for the pressure flushing simulation. An attempt was reported by Ermilov et al [84], who used the TELEMAC-MASCARET package to simulate the pressure flushing scenarios. Model results were compared with the physical model data.…”

Section: Three-dimensional Numerical Modelsmentioning

confidence: 99%

Hydraulic Flushing of Sediment in Reservoirs: Best Practices of Numerical Modeling

Lai,

Huang,

Greimann

2024

Fluids

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This article provides a comprehensive review and best practices for numerically simulating hydraulic flushing for reservoir sediment management. Three sediment flushing types are discussed: drawdown flushing, pressure flushing, and turbidity current venting. The need for reservoir sediment management and the current practices are reviewed. Different hydraulic drawdown types are described in terms of the basic physical processes involved as well as the empirical/analytical assessment tools that may be used. The primary focus has been on the numerical modeling of various hydraulic flushing options. Three model categories are reviewed: one-dimensional (1D), two-dimensional (2D) depth-averaged or layer-averaged, and three-dimensional (3D) computational fluid dynamics (CFD) models. General guidelines are provided on how to select a proper model given the characteristics of the reservoir and the flushing method, as well as specific guidelines for modeling. Case studies are also presented to illustrate the guidelines.

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Section: Three-dimensional Numerical Modelsmentioning

confidence: 99%

Hydraulic Flushing of Sediment in Reservoirs: Best Practices of Numerical Modeling

Lai,

Huang,

Greimann

2024

Fluids

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“…For instance, using one of the training videos of this study the authors managed to reconstruct the grain-scale 3D model of a riverbed section with the Structure-from-Motion technique (Ermilov et al, 2020), enabling the quantitative estimation of surface roughness. Underwater field cameras can also be used for monitoring and estimating bedload transport rate (Ermilov et al, 2022) by adapting LS-PIV and the Statistical Background Model approach. This latter videography technique may also be used with moving cameras (e.g., Hayman and Ekhlund, 2003), which enables its adaptation into our method by e.g., detecting bedload movement in the cross-section.…”

Section: Novelty and Future Workmentioning

confidence: 99%

Automated riverbed material analysis using Deep Learning on underwater images

Ermilov

Benkő

Baranya

2022

Preprint

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Abstract. The sediment of alluvial riverbeds plays a significant role in river systems both in engineering and natural processes. However, the sediment composition can show great spatial and temporal heterogeneity, even on river reach scale, making it difficult to representatively sample and assess. Indeed, conventional sampling methods in such cases cannot describe well the variability of the bed surface texture due to the amount of energy and time they would require. In this paper, an attempt is made to overcome this issue introducing a novel image-based, Deep Learning algorithm and related field measurement methodology with potential for becoming a complementary technique for bed material samplings and significantly reducing the necessary resources. The algorithm was trained to recognise main sediment classes in videos that were taken underwater in a large river with mixed bed sediments, along cross-sections, using semantic segmentation. The method is fast, i.e., the videos of 300–400 meter long sections can be analysed within minutes, with very dense spatial sampling distribution. The goodness of the trained algorithm is evaluated mathematically and via intercomparison with other direct and indirect methods. Suggestions for performing proper field measurements are also given, furthermore, possibilities for combining the algorithm with other techniques are highlighted, briefly showcasing the multi-purpose of underwater videos for hydromorphological adaptation. The paper is to show the potential of underwater videography and Deep Learning through a case study.

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“…The experimental protocol was then adapted to a flume experiment and a gravel-bed river. Ermilov et al [35] used underwater video to track the motion of bedload sediment in a flume. They analyzed the individual sediment particle movements as well as the distribution of the moving particle sizes by applying statistical theory and image processing techniques.…”

Section: Introductionmentioning

confidence: 99%

Development of the Gravel Pressure and Voice Synchronous Observation System and Application in Bedload Transport Measurement

Tian,

Yang,

Zhang

et al. 2023

Applied Sciences

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Bedload sediment transport is critical in the natural river environmental and ecological system. It is quite challenging to measure the bedload sediment transport rate in rivers with any degree of accuracy. In this study, the authors developed the Gravel Pressure and Voice Synchronous observation system (GPVS) to estimate the bedload sediment transport rate in rivers. The core of the GPVS includes an underwater high-fidelity audio recorder and pressure sensor. The audio recorder is intended to monitor the low bedload sediment transport rate, whereas the pressure sensor is utilized to detect relatively substantial bedload sediment transport. The GPVS is tested by running flume experiments with natural gravel to evaluate the system’s reliability and feasibility. Results reveal that the GPVS has a very high sensitivity for detecting bedload transport. When the bedload sediment transport rate is relatively low, the audio recorder can measure it quantitatively, showing that the system is not impacted by ambient noise.

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Bedload transport analysis using image processing techniques

Cited by 11 publications

References 52 publications

Hydraulic Flushing of Sediment in Reservoirs: Best Practices of Numerical Modeling

Hydraulic Flushing of Sediment in Reservoirs: Best Practices of Numerical Modeling

Automated riverbed material analysis using Deep Learning on underwater images

Development of the Gravel Pressure and Voice Synchronous Observation System and Application in Bedload Transport Measurement

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