Document Type : Research Paper

**Authors**

Ardakan University

**Abstract**

Estimation sediment load of rivers is the most important challenges in river engineering. So, it was addressed different models by varying structures to estimate sediment load. In this study, it was reviewed effectiveness of geo-morphometric parameters and data mining technique to predict suspended sediment load in 68 basins in two different regions of Iran. For this reason, it was run six artificial neural networks models, linear regression, K-nearest neighbor, Gaussian process, support vector machine evolutionary on two types of suspended sediment data (i.e. maximum and average sediment). To optimize models, it was used geo-morphometric parameters and river discharge as input data into model and it was used attribute reduction technique to decrease the algorithms space. Results of models evaluation indicated that models performance is difference in average and minimum sediment data, so that the best method to predict average sediment is the Gaussian model by correlation coefficient, 0.988 and root mean squared, 11.004 and the best method to predict minimum sediment is support vector machine evolutionary model by correlation coefficient, 0.966 and root mean squared, 0.171.

**Keywords**

**Main Subjects**

Asselman, N.E.M. 2000. Fitting and interpretation of sediment rating curves. *Journal of Hydrology.* 23 (4), 228-248.

Camdevyren, H. Demyr, N. Kanik, A. and Keskyn, S. 2005. Use of principal componentscores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs. *Ecological Modelling*. 181(4), 581-589.

Choi, S.U. and Lee, J., 2015. Assessment of total sediment load in rivers using lateral distribution 12.Cortes, C., Vapnik, V., 1995. Support-vector network. Mach. *Learn*. 20, 273–297.

Cobaner, M., Unal, B. and Kisi, O., 2009. Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. Journal of hydrology, 367(1), pp.52-61. method. *Journal of Hydro-environment Research,* 9 (3), pp.381-387.

Haykin, S., 1998. Neural Networks – A Comprehensive Foundation, second ed. Prentice-Hall, Upper Saddle River, NJ, pp. 26–32.

Ho, S.-Y., Shu, L.-S., Chen, J.-H., 2004. Intelligent evolutionary algorithms for large parameter optimization problems. *IEEE Trans. Evolutionary Comput*. 8 (6), 522–541.

Huang, H.L. and Chang, F.L., 2007. ESVM: Evolutionary support vector machine for automatic feature selection and classification of microarray data. *Biosystems*, 90(2), pp.516-528.

Johnson, R. A. and Wichern, D. W. 1982. Applied multivariate statistical analysis, 3rd Ed, Prentice- Hall Inc, Englewood Cliffs, USA.

Kisi, O., 2010. River suspended sediment concentration modelling using a neural differential evolution approach. *J. Hydrol.* 389 (1–2), 227–235

Kisi, O., 2012. Modeling discharge-suspended sediment relationship using least square support vector machine. *Journal of hydrology*, 456, pp.110-120.

Liu, Q.J., Shi, Z.H., Fang, N.F., Zhu, H.D. and Ai, L., 2013. Modeling the daily suspended sediment concentration in a hyperconcentrated river on the Loess Plateau, China, using the Wavelet–ANN approach. *Geomorphology*,186, pp.181-190.

Lafdani, E.K., Nia, A.M. and Ahmadi, A., 2013. Daily suspended sediment load prediction using artificial neural networks and support vector machines. *Journal of Hydrology,* 478, pp.50-62.

Najafi, G., Ghobadian, B., Tavakoli, T., Buttsworth, D.R., Yusaf, T.F. and Faizollahnejad, M., 2009. Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. *Applied Energy*, 86(5), pp.630-639.

Rajaee, T., 2011. Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. *Science of the total environment,*409 (15), pp.2917-2928.

Rajaee, T., Mirbagheri, S.A., Zounemat-Kermani, M. and Nourani, V., 2009. Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. *Science of the total environment,* 407(17), pp.4916-4927.

Sani Abade, M., Mahmoudi, S, and Taherparvar, D. (2014). Data mining applications (second edition), Niaz-e-Danesh Pub.Tehran. (In Farsi).

Talebi, A., Hajiabolghasemi, R., Hadian, M.R. and Amanian, N., 2016. Physically‐based modeling of sheet erosion (detachment and deposition processes) in complex hillslopes. *Hydrological Processes*.30(12).pp 1968–1977.

Verstraeten, G. and Poesen, J., 2001. Factors controlling sediment yield from small intensively cultivated catchments in a temperate humid climate.*Geomorphology*, 40(1), pp.123-144.

Ward, P.J., van Balen, R.T., Verstraeten, G., Renssen, H. and Vandenberghe, J., 2009. The impact of land use and climate change on late Holocene and future suspended sediment yield of the Meuse catchment. *Geomorphology*, 103(3), pp.389-400.

Wang, Y.G., Wang, S.S. and Dunlop, J., 2015. Statistical modelling and power analysis for detecting trends in total suspended sediment loads. *Journal of Hydrology*, 520, pp.439-447.

Zhu, Y.M., Lu, X.X. and Zhou, Y., 2007. Suspended sediment flux modeling with artificial neural network: an example of the Longchuanjiang River in the Upper Yangtze Catchment, China. *Geomorphology*, 84(1), pp.111-125.

Zounemat-Kermani, M., Kişi, Ö., Adamowski, J. and Ramezani-Charmahineh, A., 2016. Evaluation of data driven models for river suspended sediment concentration modeling. *Journal of Hydrology*, 535, pp.457-472.

October 2017

Pages 669-678