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

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 (r²_pred). The results indicate that ANN is the most effective method (r²_pred = 0.94), followed by SVM (r²_pred = 0.72). MLR and PLS methods are the least effective techniques (r²_pred = 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

NEHİRLERDE SEDİMENT MİKTARININ BELİRLENMESİNDE AMPİRİK MODELLEME TEKNİKLERİNİN DEĞERLENDİRİLMESİ

Abstract

Nehirlerdeki sediment taşınım süreçleri uzun yıllardır önemli bir araştırma konusu olmuştur. Nehirlerde taşınan sediment miktarı, nehrin akımı ve sediment konsantrasyonu ile güçlü bir ilişki içerisindedir. Bu çalışma, bu ilişkiyi göstermeyi ve dört farklı modelleme tekniği olan MLR, PLS, SVM ve ANN metotlarını kullanarak sediment miktarını hesaplamayı amaçlamaktadır. Türkiye’nin Doğu Akdeniz bölgesinde yer alan Göksu Nehri’ne ait akım, sediment konsantrasyonu ve sediment miktarı modellerde girdi verisi olarak kullanılmıştır. Bu çalışmanın amacı, nehir akımıyla taşınan sediment miktarının tahmin edilmesinde ANN modelleme tekniğinin etkisini değerlendirmektir. Verilerin yüzde ellisi modelin geliştirilmesi için öğrenme seti olarak, kalan veriler ise modelin validasyonu  için test seti olarak kullanılmıştır. Test setinin belirleme katsayısı (r²_pred) dikkate alınarak dört modelin performansı değerlendirilmiştir. Sonuçlar ANN’nin en etkili yöntem olduğunu (r²_pred = 0.94) ve onu SVM’nin takip ettiğini (r²_pred=0.72) göstermektedir. MLR ve PLS ise Göksu Nehri’ndeki sediment miktarının belirlenmesinde en az etkili yöntemlerdir  (r²_pred = 0.67). Bu nedenle, nehirdeki sediment miktarını tahmin etmek için en etkili yöntem, ANN’nin farklı konfigürasyonları çalışılarak araştırılmıştır.

Keywords

• Sediment miktarı,Nehir,Modelleme,ANN

References

1. 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
2. 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
3. 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
4. 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
5. Engelund, F. and Fredsoe, J. (1976). A sediment transport model for straight alluvial channels, Nordic Hydrology, 7(5), 293-306.
6. 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
7. 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
8. 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

9. 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-5-89-20051
10. MDM (Molegro Data Modeller) User Manual, 2013. http://www.clcbio.com/files/usermanuals/MDM_manual.pdf (last accessed in June 2016)
11. 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
12. 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
13. 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
14. 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)
15. Tayfur, G. (2002). Artificial neural networks for sheet sediment transport, Hydrological Sciences, 47(6), 879-892. doi: 10.1080/02626660209492997
16. 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
17. 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
18. 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
19. 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