Estimation of the Grain-Size Distribution Using Semisupervised Affinity Propagation
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 12
Abstract
The grain-size distribution can play an important role in the sediment movement and the bedload transport rate. However, it still remains an important and challenging issue in the study of river behavior. Accurate estimation of the grain-size distribution is desired, while simultaneously one expects to spend much less time on it. Recently image analysis and machine learning techniques facilitated grain identification and measurement on images. In this paper, a semisupervised affinity propagation model (SAPM) oriented to images method is proposed for automatic extraction of the grain-size distribution based on photographs sampled from Wenchuan and Yingxiu in China where landslides and mudslides usually take place. The model to estimate the grain-size distribution is developed and the corresponding algorithm is illustrated in detail. The experiments are finished in both lab and field, and the proposed algorithm is compared with traditional methods. The proposed algorithm produces much better results in estimating the grain-size distribution in comparison with other image processing methods and manual sieving methods. It is shown that SAPM is an efficient method for precisely estimating the grain-size distribution.
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Acknowledgments
The research reported in this paper is supported by the National Natural Science Foundation of China (Grant Nos. 51479128 and 61262058) and the Key Technology Research and Development Program of Sichuan Province, China (Grant No. 2014SZ0163).
References
Ackermann, F. (1980). “A procedure for correcting the grain size effect in heavy metal analyses of estuarine and coastal sediments.” Environ. Technol., 1(11), 518–527.
Adams, J. (1979). “Gravel size analysis from photographs.” J. Hydraul. Div., 105(10), 1247–1255.
Basu, S., Bilenko, M., and Mooney, R. J. (2004). “A probabilistic framework for semisupervised clustering.” Proc., 10th Association of Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) Int. Conf. on Knowledge Discovery and Data Mining, New York, 59–68.
Bathurst, J. C. (1985). “Flow resistance estimation in mountain rivers.” J. Hydraul. Eng., 625–643.
Blott, S. J., Croft, D. J., Pye, K., Saye, S. E., and Wilson, H. E. (2004). “Particle size analysis by laser diffraction.” Geol. Soc. London, 232(1), 63–73.
Buscombe, D. (2008). “Estimation of grain-size distributions and associated parameters from digital images of sediment.” Sediment. Geol., 210(1), 1–10.
Butler, J. B., Lane, S. N., and Chandler, J. H. (2001). “Automated extraction of grain-size data from gravel surfaces using digital image processing.” J. Hydraul. Res., 39(5), 519–529.
Chang, F.-J., and Chung, C.-H. (2012). “Estimation of riverbed grain-size distribution using image-processing techniques.” J. Hydrol., 440, 102–112.
Clifford, N., Robert, A., and Richards, K. (1992). “Estimation of flow resistance in gravel-bedded rivers: A physical explanation of the multiplier of roughness length.” Earth Surface Process. Landforms, 17(2), 111–126.
Frey, B. J., and Dueck, D. (2007). “Clustering by passing messages between data points.” Science, 315(5814), 972–976.
Grady, L., and Schwartz, E. L. (2006). “Isoperimetric graph partitioning for image segmentation.” IEEE Trans. Pattern Analysis Mach. Intell., 28(3), 469–475.
Graham, D. J., Reid, I., and Rice, S. P. (2005). “Automated sizing of coarse-grained sediments: Image-processing procedures.” Math. Geol., 37(1), 1–28.
Leopold, L. B. (1970). “An improved method for size distribution of stream bed gravel.” Water Resour. Res., 6(5), 1357–1366.
McEwan, I., Sheen, T., Cunningham, G., and Allen, A. (2000). “Estimating the size composition of sediment surfaces through image analysis.” Proc. Inst. Civil Eng. Water Maritime Energy, 142(4), 189–195.
Meyer, F., and Beucher, S. (1990). “Morphological segmentation.” J. Visual Commun. Image Represent., 1(1), 21–46.
Mora, C., Kwan, A., and Chan, H. (1998). “Particle size distribution analysis of coarse aggregate using digital image processing.” Cement Concrete Res., 28(6), 921–932.
Parker, G. (1990). “Surface-based bedload transport relation for gravel rivers.” J. Hydraul. Res., 28(4), 417–436.
Powell, D. M., Reid, I., and Laronne, J. B. (2001). “Evolution of bed load grain size distribution with increasing flow strength and the effect of flow duration on the caliber of bed load sediment yield in ephemeral gravel bed rivers.” Water Resour. Res., 37(5), 1463–1474.
Richardson, W. (2008). “Sobolev gradient preconditioning for image-processing PDEs.” Commun. Numer. Meth. Eng., 24(6), 493–504.
Shen, H. W., and Lu, J.-Y. (1983). “Development and prediction of bed armoring.” J. Hydraul. Eng., 611–629.
Wagstaff, K., Cardie, C., Rogers, S., and Schrödl, S. (2001). “Constrained k-means clustering with background knowledge.” Proc., 18th Int. Conf. on Machine Learning, Vol. 1, 577–584.
Wilcock, P. R., and Crowe, J. C. (2003). “Surface-based transport model for mixed-size sediment.” J. Hydraul. Eng., 120–128.
Wilcock, P. R., and McArdell, B. W. (1993). “Surface-based fractional transport rates: Mobilization thresholds and partial transport of a sand-gravel sediment.” Water Resour. Res., 29(4), 1297–1312.
Xue, Y., and Yang, S. X. (2005). “Image segmentation using watershed transform and feed-back pulse coupled neural network.” Proc., Artificial Neural Networks: Biological Inspirations, Springer, Amsterdam, Netherlands, 531–536.
Zhang, D., Chen, S., Zhou, Z.-H., and Yang, Q. (2008). “Constraint projections for ensemble learning.” Proc., 23rd AAAI Conf. on Artificial Intelligence (AAAI’08), Chicago, 758–763.
Zhou, Z.-H., and Tang, W. (2006). “Clusterer ensemble.” Knowledge-Based Syst., 19(1), 77–83.
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Published In
Journal of Hydrologic Engineering
Volume 20 • Issue 12 • December 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: May 27, 2014
Accepted: Mar 19, 2015
Published online: Jun 1, 2015
Discussion open until: Nov 1, 2015
Published in print: Dec 1, 2015
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