[1]. Ferguson, R., (2010). Time to abandon the Manning equation? Earth Surf. Process. Landf. 35.
[2]. SUMER, B. MUTLU (2013). “LECT URE NOTES ON TURBULENCE.”, Technical University of Denmark.
[3]. Powell, D. M. (2014). Flow resistance in gravelbed rivers: Progress in research. Earth-Science Reviews, 136, 301-338.
[4]. Simons, D. B., & Richardson, E. V. (1996). Resistance to flow in alluvial channels. US Government Printing Office.
[5]. Ackers, P. & White, W. R. (1973). «Sediment transport: new approach and analysis». Journal of the Hydraulics Division, 99 (hy11).
[6]. Hammond, F. D., Heathershaw, A. D., and Langhorne, D. N. (1984) .‘‘A comparison between Shields’ threshold criterion and the Henderson movement of loosely packed gravel in a tidal channel.’’ Sedimentology, 31, .26–15
[7]. Colosimo, C., Copertino, V. A., & Veltri, M. (1986). «Average velocity estimation in gravel-bed rivers». In Proc., 5th IAHR-APD Congress (pp. 1-15).
[8]. Wilson, K. C. (1989). Mobile-bed friction at high shear stress. Journal of Hydraulic Engineering, 115(6), .038-528
[9]. Yalin, M. S. “River Mechanics, 219 pp.” (1992).
[10]. Sumer, B. M., Kozakiewicz, A., Fredsøe, J., & Deigaard, R. (1996). «Velocity and concentration profiles in sheet-flow layer of movable bed». Journal of Hydraulic Engineering, 122(10), 549-558.
[11]. KIM J.S, LEE .J., KIM W. , Yong J. K. (2010) Roughness coefficient and its uncertainty in gravelbed river, Water Science and Engineering
[12]. Yang, H. C., & Chang, F. J. (2005). «Modeling combined open channel flow by artificial neural networks». Hydrological Processes, 19 (18), 37473762.
[13]. Yuhong, Z., & Wenxin, H. (2009). «Application of artificial neural network to predict the friction factor of open channel flow». Communications in Nonlinear Science and Numerical Simulation, 14(5), 2373-2378.
[14]. Shayya, W. H., & Sablani, S. S. (1998). «An artificial neural network for non-iterative calculation of the friction factor in pipeline flow». Computers and electronics in agriculture, 21(3), 219-228.
[15]. Abdeen, M. A. M. (2004). «Artificial neutral network model for predicting the impact changing water structures’ locations on the hydraulic performance of branched open channel system». Mechanics and Mechanical Engineering,7(2), 179-192.
[16]. Zahiri, A., & Dehghani, A. A. (2009). «Flow discharge determination in straight compound channels using ANN». World Academy of Science, Engineering and Technology, 58, 12-15.
[17] Azamathulla H. Md., (2012), “ Gene-expression programming to predict friction factor for Southern Italian rivers, Journal of
Neural Computing and Applications, 23 (8), 1421–142
[19]. Bateni, S. M., Borghei, S. M., & Jeng, D. S. (2007). «Neural network and neuro-fuzzy assessments for scour depth around bridge piers». Engineering Applications of Artificial Intelligence, 20(3), 401-414.
[20].Begum, S. A., Fujail, A. M., & Barbhuiya, A. K. (2012). «Artificial neural network to predict equilibrium local scour depth around semicircular bridge abutments». 6th SASTech, Malaysia, Kuala Lumpur.
[21].Kazeminezhad, M. H., Etemad-Shahidi, A., & Bakhtiary, A. Y. (2010). «An alternative approach for investigation of the wave-induced scour around pipelines». Journal of Hydroinformatics, 12(1), 51-65.
[22].Ghazanfari-Hashemi, S., Etemad-Shahidi, A., Kazeminezhad, M. H., & Mansoori, A. R. (2011). «Prediction of pile group scour in waves using support vector machines and ANN». Journal of Hydroinformatics, 13(4), 609-620.
[23]. Zaji, A. H., & Bonakdari, H. (2015). Application of artificial neural network and genetic programming models for estimating the longitudinal velocity field in open channel junctions. Flow Measurement and Instrumentation, 41, 81-89.
[25]. Sadegh Safari M., Aksoy, H., & Mohammadi, M. (2016). Artificial neural network and regression models for flow velocity at sediment incipient deposition. Journal of Hydrology, 541, 1420-1429.
[26]. Johnson, P. A. & Ayyub, B. M. (1996) «Modeling uncertainty in prediction of pier scour». Journal of Hydraulic.
[27]. Muzzammil, M. (2000). «ANFIS approach to the scour depth prediction at a bridge abutment». Journal of Hydroinformatics 12 (4):474-485.
[28]. Muzzammil, M., and J. Alam. (2011). «ANFISbased approach to scour depth prediction at abutments in armored beds». Journal of Hydroinformatics 13 (4):699-713.
[29]. Zanganeh, M., Yeganeh-Bakhtiary, A., & Bakhtyar, R. (2011). Combined particle swarm optimization and fuzzy inference system model for estimation of current-induced scour beneath marine pipelines. Journal of Hydroinformatics, 13(3), 558-573.
[30]. Bateni, S. M., & Jeng, D. S. (2007). Estimation of pile group scour using adaptive neuro-fuzzy approach. Ocean Engineering, 34(8), 1344-1354.
[31]. Ozger, M. (2009) «Comparison of fuzzy inference systems for stream flow prediction». Hydrological Sciences Journal 54(2), 261–273.
[32]. Azmathullah, H. Md., Ghani, A. A. & Zakaria, N. A. (2009) «.ANFIS-based approach to predicting scour location of spillway». Water management.
[33]. McCulloch, W. S., & Pitts, W. (1943). «A logical calculus of the ideas immanent in nervous activity». The bulletin of mathematical biophysics, 5(4), 115-133.
[34]. Rogers, L.L. & Dowla, F.U. (1994) «Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling». Water Resources Research. 30 (2), 457–481.
[35]. Hecht-Nielsen, R. (1978). «Kolmogorov’s mapping neural network existence theorem». In Proceedings of the international conference on Neural Networks (Vol. 3, pp. 11-13). New York: IEEE Press.
[36]. Jang JSR. (1993)».ANFIS Adaptive-Network-Based Fuzzy Inference systems». IEEE Trans System Man Cybern; 23(3): 665-685.
[37]. Chiu S.L (1994). «Fuzzy model identification based on cluster estimation. Intelligent Fuzzy Systems»; 2:234–244. Engineering 122(2), 66–72.