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Comparison of software accuracy to estimate the bed grain size distribution from digital images: A test performed along the Rhine River

Abstract : The quantification of the bed grain size distribution (GSD) of river surfaces is primarily conducted through manual approaches in the field. These methods are time consuming and not able to accurately represent the spatial diversity of the grain size distribution of rivers. Recently, several software programs and procedures have been developed using semi-automatic and automatic methods to estimate bed GSD from digital imagery. The purpose of this study is to compare softwares accuracy between reference GSDs and estimated GSDs using geometric approaches (Basegrain software and a procedure developed on ImageJ), statistical approaches (digital grain size [DGS] and PebbleCounts softwares), and a machine learning framework (SediNet). This study evaluates ten digital images recorded along the Rhine River downstream of the city of Basel. The results showed that all software programs considerably underestimated the manually measured GSDs. Nevertheless, it is possible to significantly improve the estimation of bed GSD by applying calibration laws. Both DGS and Basegrain softwares are reliable to estimate the GSD, while the three others softwares are accurate for percentiles equal and higher than the D50. After linear regression correction, the mean normalized root mean square error of percentile errors did not exceed 13% for DGS and Basegrain software, while the others did not exceed 22% for percentiles coarser than the D50.
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https://hal-cnrs.archives-ouvertes.fr/hal-03634704
Contributor : Valentin Chardon Connect in order to contact the contributor
Submitted on : Wednesday, April 13, 2022 - 9:59:25 AM
Last modification on : Thursday, April 14, 2022 - 3:25:51 AM

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Valentin Chardon, Guillaume Piasny, Laurent Schmitt. Comparison of software accuracy to estimate the bed grain size distribution from digital images: A test performed along the Rhine River. River Research and Applications, Wiley, 2022, 38 (2), pp.358 - 367. ⟨10.1002/rra.3910⟩. ⟨hal-03634704⟩

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