پیش‏ بینی رواناب با استفاده از مدل‏ های هوشمند

[1].Danandehmehr A, Majdzadeh Tabatabai MR. Prediction of daily discharge trend of river flow based on genetic programming, J. of Water and Soil. 2010; 24 (2): 325-333. [In Persian].

[2]. Kia SM.Soft computing using MATLAB, Kianrayaneh sabz press, 2011.P. 623. [In Persian].

[3]. Yosefi M, Talebi A, Poorshareiati R. Application of artificial intelligence in water and soil sciences, Yaz University Press, 2014: P. 516. [In Persian].

[4]. Nayak PC, Sudheer KP, Rangan, DM, Ramasastri KS. A neuro-fuzzy computing technique for modeling hydrological time series. J. of Hydrology. 2004; 29: 52–66.

[5]. Motamednia M, Nohegar A, Malekian A, Asadi H, Tavasoli A, Safari M, Karimi Zarchi K. Daily river flow forecasting in a semi-arid region using two data- driven, Desert. 2015; 20-1: 11-2.

[6]. Noori N, Kalin L, 2016. Coupling SWAT and ANN models for enhanced daily streamflow prediction, J. of Hydrology. 2016; 533: 141–151.

[7]. Nabezadeh M, Mosaedi A, Hessam M, Dehghani AA, Zakerneya M, Holghi M, 2012. Investigating efficiency fuzzy logic to predict daily river flow, Iran-Watershed Management Science & Engineering. 2012; 5(17): 7-14. [In Persian].

[8]. Nohegar A, Motamednia M, Malekian A. Daily river flood mresentative watershed, Physical Geography Research Quarterly. 2016; 48(3): 367-383. [In Persian].

[9].Mahdavi M, Applied Hydrology, First volume, fourth edition, Tehran university press, 2003: P. 364. [In Persian].

[10].Imrie CE, Durucan S, Korre A. River fow prediction using artificial neural networks: generalisation beyond the calibration range, J. of Hydrology. 2000; 233: 138-153 pp.

[11].Food I, Kartman N. Neural network in civil engineering: principal and understanding, J. of computing in civil engineering. 1996; 8 (2): 131-148.

[12].Kaastra I. Boyd MS. Forecasting futures trading volume using neural networks, The J. of Futures Markets.1995; 15(8): 953-970.

[13].Gharaei-Manesh S, Fathzadeh A, Taghizadeh-Mehrjardi R. Comparison of artificial neural network and decision tree models in estimating spatial distribution of snow depth in a semi-arid region of Iran, Cold Regions Science and Technology, 2016; 122: 26–35 pp.

[14].Kakaei Lafdani E, Moghaddamnia A, Ahmadi A. Daily suspended sediment load prediction using artificial neural networks and support vector machines, J. of Hydrology, 2013; 478: 50–62.

[15].Mahjouri N, Integrating support vector regression and a geomorphologic artificial neural network for daily rainfall-runoff modeling, Applied Soft Computing, 2014; 38: 329–345 pp.

[16].Dawson CW, Wilby R.L., 2001. Hydrological modeling using artificial neural network, Progress in Physical Geography. 2001; 25: 80–108.

[17].Tokar A S, Markus M. Precipitation rainfall-runoff modeling using artificial neural network and conceptual models, J.of Hydrologic Engineering. 2000; 5(2):156-161 pp.

[18].Dibike Y, Solomatine D. River flow forecasting using artificial neural networks. J. of Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere. 2001; 26: 1–8.

[19].Mendez MC, Wenceslao G, ManuelPF, José Manuel LP, Roman L. Modelling of the monthly and daily behaviour of the runoff of the Xallas river using Box-Jenkins and neural networks methods. J. of Hydrology. 2004; 1685-1694.

[20].Melesse AM, Ahmad S, McClain ME, Wang X, Lim YH. Suspended sediment load prediction of river systems: An Artificial Neural Networks Approach, Agricultural Water Management. 2011; 98(5): 855-866.

[21].Zounemat-Kermani M, Teshnehlab M. Using adaptive neuro-fuzzy inference system for hydrological time series prediction, Applied Soft Computing. 2008; 8(2): 928-936.