Document Type : Research Paper


1 MSC of watershed management, Faculty of Desert and Natural Resources, Yazd University, Iran

2 Academic staff of agricultural department of Payamnoor university, Iran


Suspended sediment estimation is an important factor from different aspects including, farming, soil conservation, dams, aquatic life, as well as various aspects of the research. There are different methods for suspended sediment estimation. This study aims to estimate suspended sediment using feed forward neural network with error back propagation with Levenberg-Marquardt back propagation algorithm and compare the results with best sediment rating curves among commonly used sediment rating curves, including: linear, seasonal, monthly and Mean load within discharge classes. To attain this, the sediment discharge and the corresponding water discharge data for ten hydrometric stations of Lorestan province of Iran were used. In next step different methods of sediment rating curves along with different correction factors, a total of 20 methods were applied to data. Results showed among examined methods; monthly rating curve with MUVE correction factor has been selected as best, based on Nash and Sutcliffe index and accuracy index. Then results of estimating sediment load by using selected sediment rating curve were compared with the results of the neural network. Mean-square error and Nash and Sutcliffe index were applied to select more appropriate method. The results showed the suitability of the feed forward neural network error propagation in compare with sediment rating curves.



[1] Akbari, Z. (2010). Performance of the decision tree and regression model to estimate the amount of sediment in the dam area of Ilam, Master's thesis, Department of Natural Resources desert Studies, Yazd University (In Farsi).
[2] Arabkhedri, M., Hakimkhani, Sh. and Varvani, J. (2004). The Validity of extrapolation methods in estimation of annual mean suspended sediment yield (17 Hydrometric Stations), Journal of Agricultural Science and Natural Resources, 11(3), 123-131 (In Farsi).
[3] Asselman, N.E.M. (2002). Fitting and interpretation of sediment rating curves, Journal of Hydrology , 234, 228-248.
[4] Barzegar, F. (2004). Comparison of methods to estimate suspended sediment (Case Study: Qezel Ozan), MSc Thesis, Department of Natural Resources, Tehran University, 120pp (In Farsi).
[5] Basheer, I.A. and Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application, Journal of Microbiological Methods, 43, 3-31.
[6] Bauer, P., Nouak, S. and Winkler, R. (2007). Fuzzy Mathematical Methods for soil survey and Land Evaluation, Journal  of soil science, 40, 477-492.
[7] Caniani, D., Pascale, S., Sdao, F. and Sole, A. (2008). Neural networks and landslide susceptibility: a case study of the urban area of Potenza, Natural Hazards, 45, 55-72.
[8] Cohn, T.A., Caulder, D.A., Gilroy, E.J., Zynjuk, L.D. and Summers, R.M. (1992). The validity of a sample statistical model for estimation fluvial constituent loads: an empirical study involving nutrient loads entering Chesapeake Bay, Water Resource Research, 28(9), 2353-2363.
[9] Dastorani, M.T., Azimi Fashi, Kh., Talebi, A. and Ekhtesasi, M.R. (2012). Suspended sediment estimation using Artificial Neural Network (Case Study: Jamyshan watershed in Kermanshah), The third year of watershed management Journal, 6, 61-74 (In Farsi).
[10] Dehghani, A., Zanganeh, M., Mosaedi, A. and Kohestani, N. (2009). Comparison Estimate suspended loud with two method Sediment Rating curve and Artificial Neural Network (Case Stady: Dough River, Golestan Province), Issue science agriculture and natural source, 16(1), 266-276 (In Farsi).
11] Dehghani, A., Mohammad Malik, M. and Hezarjarib, A. (2010). Behesht Abad river suspended sediment estimation using artificial neural networks, Journal of Soil and Water Conservation, 17(1), 159-168 (In Farsi).
[12] Feiznia, S., Ghafari, G., Karimizadeh, K. and Tabatabaezadeh, M. (2011). Determination of the Most Suitable Method for Estimation of Suspended Sediment in Hydrometric Stations Upland of Latian and Taleghan Dams, Journal of Natural Environment, Iranian Journal of Natural Resources, 64(3), 231-242 (In Farsi).
[13] Hagan, M.T., Demuth, H.B. and Beale, M.H. (1996). Neural Network Design, global book store, (Spring, TX, U.S.A.), ISBN: 0971732108 / 0-9717321-0-8,734 pp.
[14] Hajebakhsh, P. (2011). Bed sediment load estimated using regression decision trees and comparison with experimental method, MSC Thesis, Civil Faculty, Yazd University (In Farsi).
[15] Hasonizadeh, H., Fazlalizadeh, M., Nekoyi, F and Shirdeli, A. (2012). Prediction density sediment in Karkheh River with use from neural network software, International conference 9th engineering river, Ahwaz, Chamran University (In Farsi).
[16] Heikki Koivo, N. (2008). NEURAL NETWORKS: Basics using MATLAB Neural Network Toolbox, 59 pp.
[17] Hsu, K., Gupta, H.V. and Sorooshian, S. (1995). Artificial neural network modeling of the rainfall-runoff process, Water Resources Research, 31(10), 2517-2530.
[18] Iadanza, C. and Napolitano, F. (2006). Sediment transport time series in the Tiber River, Physics and Chemistry of the Earth, 31, 1212-1227.
[19] Jansson, M.B. (1996). Estimating a sediment rating curves of the Reventazon River at Palamo using logged mean loads within discharge classes,Journal of Hydrology, 183(4), 227-241.
[20] Jones, K.R., Berney, O., Carr, D.P., and Barret, E.C. (1981). Arid zone hydrology for agricultural development, FAO Irrigation and Drainage Paper, 37, 271.
[21] Kao, Sh.J., Lee, T.Y., and Milliman, J.D. (2005). Calculating highly fluctuated suspended sediment fluxes from mountainous rivers in Taiwan, TAO, 16(3), 653-675.
[22] Koch, R.W. and Smillie, G.M. (1986). Comment on river loads underestimated by rating curves, Water Resources Research, 22(13), 2121-2122.
[23] Lee, S., Ryu, J.H., Lee, M.J. and Won, J.S. (2006). The Application of artificial neural networks to landslide susceptibility mapping at Jan hung, Korea, Mathematical Geology, 38(2), 199-220.
[24] Mosaedi, A. (1998). Hydrological sizing of sediment reservoir system for irrigation and water supply. Ph.D. Thesis, Faculty of Civil Eng, Technical University of Budapest, Hungary, 101pp.
[25] Rajaee, T., Mirbagheri, S.A. and Kermani, M.Z. (2009). Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models, Science of the total environment, 407, 4916-4927.
[26] Salajegeh, A. and Fathabadi, A. (2009). Assessment Possible Estimate suspended load Karaj River with beneficiary from fuzzy-logic and neural network, Range and Watershed (Iran natural source), 62(2), 271-282 (In Farsi).
[27] Shabani, M. and Shabani, N. (2012). Estimation of Daily Suspended Sediment Yield Using Artificial Neural Network and Sediment Rating Curve in Kharestan Watershed, Iran, Australian Journal of Basic and Applied Sciences, 6(12), 157-164.
[28] Telvari, A. (2003). The relationship between suspended sediment and certain properties in the area Dez and Karkheh Basin, Research and development, 56-57, 56-61 (In Farsi).
[29] Thomas, R.B. (1985). Estimating total suspended sediment yield with probability sampling, Water Resources Research, 21(9), 1381-1388.
[30] Toloie, S., Hossenzadeh, D., Ghorbani, M., Fakherifard, A., and Salmasi, F. (2011). Estimate temporal and spatial suspended loud river Ajichai with use from Geostatistics and Artificial neural network, Issue science water and soil, 21(4), 93-104 (In Farsi).
[31] Vali, A., Ramesht, M., Siff, A. and Ghazavi, R. (2011). Comparison efficiency Artificial Neural Network models and regression for prediction flow sediment loud (Case Study: catchment basin Samandegan), Issue Geographic and Environment Schematization, 44(4), 19-34 (In Farsi).