A refined automated grain sizing method for estimating river-bed grain size distribution of digital images

Natural bed topography and habitat is affected by the composition of gravels in various shapes and sizes. Traditional measurement methods for grain size distribution are time-consuming and labor-intensive. Recent advances in image processing techniques facilitate automated grain size measurement through digital images. This study introduces a refined automated grain sizing method (R-AGS) incorporating a neural fuzzy network for automatically estimating the grain size distribution, specifically for digital images composed of grains ranging from 16 mm to 512 mm. A total of 130 digital images captured from the Lanyang river-bed in northeast Taiwan are used to assess the R-AGS performance. We demonstrate the neural fuzzy network can adequately identify the binary threshold, which is a crucial parameter of the AGS procedure, and the proposed R-AGS can be intelligibly used for automated accurate estimation of grain size distribution with much less labor-intensiveness for each digital image. Moreover, it is easy to re-construct the network by updating rule nodes for image samples significantly different from this study; consequently its applicability and practicability could be expanded.