Evaluation of Empirical Modelling Techniques for the Estimation of Sediment Amount in Rivers

Başak GÜVEN, Zeynep AKDOĞAN
676 238

Abstract


The sediment transport processes of streams have been the subject of research for many years. Sediment amount carried by a river is strongly correlated with the river’s flow rate and sediment concentration. This study aims to represent this correlation and to estimate the sediment amount using four different modelling techniques: MLR, PLS, SVM, and ANN. Records of river flow, sediment concentration and sediment amount obtained from the Göksu River, located in the Eastern Mediterranean region of Turkey, are used as input data in the models. The aim of is this study is to evaluate the effectiveness of ANN modelling in the estimation of sediment amount carried by river flow. Fifty percent of the data are used as training set to develop the models. The other half of the data is used for verification set. The performance of the four models is evaluated by determination coefficient of prediction set (r2pred). The results indicate that ANN is the most effective method (r2pred = 0.94), followed by SVM (r2pred = 0.72). MLR and PLS methods are the least effective techniques (r2pred = 0.67) for estimating sediment amount in the Göksu River. Therefore, ANN approach is further studied to propose the best configuration for the prediction of river sediment amount.


Keywords


sediment amount; river; modelling; ANN

Full Text:

PDF


References


Abrahart, R.J. and White, S.M. (2001). Modelling sediment transfer in Malawi: Comparing backpropagation neural network solutions against a multiple linear regression benchmark using small data sets, Physics and Chemistry of the Earth (B), 26(1), 19-24. doi: 10.1016/S1464-1909(01)85008-5

Arı Güner, H.A., Yüksel, Y. and Çevik, E.Ö. (2013). Longshore sediment transport-field data and estimations using neural networks, numerical model, and empirical models, Journal of Coastal Research, 29(2), 311 – 324. doi: http://dx.doi.org/10.2112/JCOASTRES-D-11-00074.1

Bhattacharya, B., Price, R.K. and Solomatine, D.P. (2005). Data-driven modelling in the context of sediment transport, Physics and Chemistry of the Earth, 30(4), 297–302. doi:10.1016/j.pce.2004.12.001

Dietrich, C.R., Green, T.R. and Jakeman, A.J. (1999). An analytical model for stream sediment transport: application to Murray and Murrumbidgee river reaches, Australia, Hydrological Processes, 13(5), 763-776. doi: 10.1002/(SICI)1099-1085(19990415)13:5<763::AID-HYP779>3.0.CO;2-C

Engelund, F. and Fredsoe, J. (1976). A sediment transport model for straight alluvial channels, Nordic Hydrology, 7(5), 293-306.

Jarritt, N.P. and Lawrence, D.S.L. (2007). Fine sediment delivery and transfer in lowland catchments: Modelling suspended sediment concentrations in response to hydrological forcing, Hydrological Processes, 21(20), 2729-2744. doi: 10.1002/hyp.6402

Kettner, A.J. and Syvitski, J.P.M. (2008). HydroTrend v.3.0: A climate-driven hydrological transport model that simulates discharge and sdiment load leaving a river system, Computers & Geosciences, 34(10), 1170-1183. doi:10.1016/j.cageo.2008.02.008

Kisi, O. (2012). Modeling discharge-suspended sediment relationship using least square support vector machine, Journal of Hydrology, 456–457, 110–120. doi:10.1016/j.jhydrol.2012.06.019

Krause, P., Boyle, D.P. and Base, F. (2005). Comparison of different efficiency criteria for hydrological model assessment, Advances in Geosciences, 5, 89–97. doi:10.5194/adgeo-5-89-2005

MDM (Molegro Data Modeller) User Manual, 2013. http://www.clcbio.com/files/usermanuals/MDM_manual.pdf (last accessed in June 2016)

Nelson, P.A., Smith, J.A. and Miller, A.J. (2006). Evolution of channel morphology and hydrologic response in an urbanizing drainage basin, Earth Surface Processes and Landforms, 31(9), 1063-1079. doi: 10.1002/esp.1308

Roy, K., Kar, S. and Ambure, P. (2015). On a simple approach for determining applicability domain of QSAR models, Chemometrics and Intelligent Laboratory Systems, 145, 22-29. doi: http://dx.doi.org/10.1016/j.chemolab.2015.04.013

Shi, Z.H., Ai, L., Li X., Huang, X.D., Wu, G.L. and Liao, W. (2013). Partial least-squares regression for linking land-cover patterns to soil erosion and sediment yield in watersheds, Journal of Hydrology, 498, 165–176. doi:10.1016/j.jhydrol.2013.06.031

Sinnakaudan, S. K., Ghani, A. A., Ahmad, M. S. S. and Zakaria N. A. (2006). Multiple linear regression model for total bed material load prediction, Journal of Hydraulic Engineering, 132(5), 521-528. doi: 10.1061/(ASCE)0733-9429(2006)132:5(521)

Tayfur, G. (2002). Artificial neural networks for sheet sediment transport, Hydrological Sciences, 47(6), 879-892. doi: 10.1080/02626660209492997

Van Maanen, B., Coco, G., Bryan, K. R. and Ruessink, B. G. (2010). The use of artificial neural networks to analyze and predict alongshore sediment transport, Nonlinear Processes in Geophysics, 17(5), 395–404. doi:10.5194/npg-17-395-2010

Yang, C.T., Marsooli, R. and Aalami, M.T. (2009). Evaluation of total load sediment transport formulas using ANN, International Journal of Sediment Research, 24(3), 274-286. doi: 10.1016/S1001-6279(10)60003-0

Yenigün, K., Bilgehan, M., Gerger, R. and Mutlu, M. (2010). A comparative study on prediction of sediment yield in the Euphrates basin, International Journal of the Physical Sciences, 5(5), 518-534. doi: 2B15E0C25933

Yitian, L. and Gu, R.R. (2003). Modeling flow and sediment transport in a river system using an Artificial Neural Network, Environmental Management, 31(1), 122–134. doi: 10.1007/s00267-002-2862-9




Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.