| Autor: |
Fi-John Chang, Chang-Han Chung |
| Rok vydání: |
2013 |
| Předmět: |
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| Zdroj: |
Journal of Hydrology. 486:224-233 |
| ISSN: |
0022-1694 |
| DOI: |
10.1016/j.jhydrol.2013.01.026 |
| Popis: |
Summary 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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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