Revisiting the automated grain sizing technique (AGS) for characterizing grain size distribution

The automated grain sizing technique (AGS) has been widely used to characterize grain size distribution of a channel bed. A handful number of literatures have been made available in portraying the wide range of AGS application for river and coastal studies. However, the accuracy of this technique is...

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Bibliographic Details
Published in:International journal of river basin management 2023-01, Vol.21 (1), p.89-98
Main Authors: Sulaiman, Mohd Sofiyan, Zainal Abidin, Roslan, Zakaria, Nor Azazi, Ahmad, Mohammad Fadhli, Fitriadhy, Ahmad, Jusoh, Ahmad
Format: Article
Language:English
Subjects:
Accuracy
Automated grain sizing
Automation
Counting methods
Edge detection
Grain size
Grain size distribution
Image segmentation
Particle size
Pebbles
relief distortion
river bed material
River beds
sieving
Size distribution
Sizing
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Summary:The automated grain sizing technique (AGS) has been widely used to characterize grain size distribution of a channel bed. A handful number of literatures have been made available in portraying the wide range of AGS application for river and coastal studies. However, the accuracy of this technique is subject to further validation and verification. The accuracy of the AGS technique is lessened due to distortions of image, relief, or tilt. This paper discusses the consistency of the AGS technique at different ground sampling distances, and the implementation of correction factors to modify the grain size distribution (GSD) curve of the AGS technique on fine and coarse fractions. Through a discrepancy ratio test, the GSD curve from the AGS technique was compared with those of the conventional sieving and pebble-counting methods. It was observed that relief distortion did not have a significant impact on the GSD curve. However, textural presence in sediment particles led to 'over-segmentation,' which complicated the edge detection of an individual particle. The introduction of correction factors, using least square regression equation, was able to correct those errors by reducing and maintaining the discrepancy ratio to 0.688-1.283 for fine fractions, and 0.758-1.125 for coarse fractions.
ISSN:1571-5124
1814-2060
DOI:10.1080/15715124.2021.1917585