References
- Azamathulla, H.M. (2013). “Gene-expression programming to predict friction factor for Southern Italian Rivers.” Neural Comput. Appl., 23(5), 1421–1426. doi:https://doi.org/10.1007/s00521-012-1091-2.
- Azimi, H., Bonakdari, H., and Ebtehaj, I. (2019). “Gene expression programming-based approach for predicting the roller length of a hydraulic jump on a rough bed.” ISH J. Hydraul. Eng., 1–11. doi:https://doi.org/10.1080/09715010.2019.1579058.
- Bengio, Y. (2009). “Learning deep architectures for AI.” Found. Trends® Mach. Learn., 2(1), 1–127. doi:https://doi.org/10.1561/2200000006.
- Candel, A., Parmar, V., LeDell, E., and Arora, A. (2018). “Deep learning with H2O.” http://h2o:ai/resources.
- Chai, T., and Draxler, R.R. (2014). “Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature.” Geosci. Model Dev., 7, 1247–1250. doi:https://doi.org/10.5194/gmd-7-1247-2014.
- Chanson, H., and Carvalho, R.F. (2015). “Hydraulic jumps and stilling basins.” Energy dissipation in hydraulic structures, CRC Press, Leiden, Netherlands, 65–104. In: Hubert Chanson (ed.), School of Civil Engineering, University of Queensland, Brisbane, Australia, ISBN 9781138027558 - CAT# K25663. Series: IAHR Monographs, 2015.
- Endres, L.A.M., (1990). “Contribution to development a system for acquisition and processing of instantaneous pressure data in laboratory.” Master thesis, Institute for Hydraulic Research. Federal University of Rio Grande do Sul, Porto Alegre [In Portuguese].
- Farhoudi, F., Sadat-Helbar, S.M., and Aziz, N. (2010). “Pressure fluctuation around chute blocks of SAF Stilling basins.” J. Agr. Sci. Tech., 12, 203–212.
- Fiorotto, V., and Rinaldo, A. (1992). “Fluctuating uplift and lining design in spillway stilling basins.” J. Hydraul. Eng., 118(4), 578–597. doi:https://doi.org/10.1061/(ASCE)0733-9429(1992)118:4(578).
- Ghorbani, M.A., Deo, R.C., Yaseen, Z.M.K., Mahasa, H.K., and Mohammad, B. (2017). “Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: Case study in North Iran.” Theor. Appl. Climatol., 133, 1119–1131. doi:https://doi.org/10.1007/s00704-017-2244-0.
- Güven, A., Gunal, M., and Çevik, A. (2006). “Prediction of pressure fluctuations on sloping stilling basins.” Can. J. Civ. Eng., 33(1), 41–48. doi:https://doi.org/10.1139/l06-101.
- Habibzadeh, A., Loewen, M.R., and Rajaratnam, N. (2014). “Mean flow in a submerged hydraulic jump with baffle blocks.” J. Eng. Mech., 140(5), 1–15. doi:https://doi.org/10.1061/(ASCE)EM.1943-7889.0000713.
- Houichi, L., Dechemi, N., Heddam, S., and Achour, B. (2013). “An evaluation of ANN methods for estimating the lengths of hydraulic jumps in U-shaped channel.” J. Hydroinf., 15(1), 147–154. doi:https://doi.org/10.2166/hydro.2012.138.
- Karbasi, M., and Azamathulla, H.M. (2016). “GEP to predict characteristics of a hydraulic jump over a rough bed.” KSCE J. Civil Eng., 20(7), 3006–3011. doi:https://doi.org/10.1007/s12205-016-0821-x.
- LeCun, Y., Cortes, C., and Burges, C.J.C. (1998). “The MNIST Database.”
- Legates, D.R., and Mccabe, G.J. (2013). “A refined index of model performance: A rejoinder.” Int. J. Climatol., 33, 1053–1056. doi:https://doi.org/10.1002/joc.3487.
- Lopardo, R.A., Fattor, C.A., Lopardo, M.C., and Casado, J.M. (2004, April). “Instantaneous pressure field on a submerged jump stilling basin.” Proc. Int. Conf. Hydraulics of Dams and River Structures, F. Yazdandoost and J. Attari, eds., Tehran, Iran, 26–28 April. Part I: A.A. Balkema Publishers, 133–138. London, UK.
- Marques, M.G., Drapeau, J., and Verrette, J.L. (1997). “Pressure fluctuation coefficient in a hydraulic jump.” Braz. J. Water Resour., 2(2), 45–52. In Portuguese.
- Naseri, M., and Othman, F. (2012). “Determination of the length of hydraulic jumps using artificial neural networks.” Adv. Eng. Software., 48, 27–31. doi:https://doi.org/10.1016/j.advengsoft.2012.01.003.
- Padulano, R., Fecarotta, O., Giudice, G.D., and Carravetta, A. (2017). “Hydraulic design of a USBR Type II Stilling basin.” J. Irrig. Drain Eng., 143(5), 04017001. doi:https://doi.org/10.1061/(ASCE)IR.1943-4774.0001150.
- Pinheiro, A.A.N., (1995). “Hydrodynamic actions in thresholds for energy dissipation basin by hydraulic jumps.” Submitted for the Doctor of Civil Engineering degree, Technical University of Lisbon, Portugal [In Portuguese].
- Roushangar, K., Akhgar, S., Salmasi, F., and Shiri, J. (2014a). “Modeling energy dissipation over stepped spillways using machine learning approaches.” J. Hydrol., 508, 254–265. doi:https://doi.org/10.1016/j.jhydrol.2013.10.053.
- Roushangar, K., and Homayounfar, F. (2019). “Prediction characteristics of free and submerged hydraulic jumps on horizontal and sloping beds using SVM method.” KSCE J. Civil Eng., 23(11), 4696–4709. doi:https://doi.org/10.1007/s12205-019-1070-6.
- Roushangar, K., Mouaze, D., and Shiri, J. (2014b). “Evaluation of genetic programming-based models for simulating friction factor in alluvial channels.” J. Hydrol., 517, 1154–1161. doi:https://doi.org/10.1016/j.jhydrol.2014.06.047.
- Saggi, M.K., and Jain, S. (2019). “Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning.” Comput. Electron. Agric., 156, 387–398. doi:https://doi.org/10.1016/j.compag.2018.11.031.
- USBR. (1987). “Spillway.” “Design of small dams”. A water resources technical publication, US Government Printing Office, Washington, 339–434.
- Willmott, C.J., and Matsuura, K. (2005). “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance.” Clim. Res., 30, 79–82. doi:https://doi.org/10.3354/cr030079.
- Yamini, O.A., Kavianpour, M.R., Mousavi, S.H., Movahedi, A., and Bavandpour, M. (2018). “Experimental investigation of pressure fluctuation onthe bed of compound flip buckets.” ISH J. Hydraul. Eng., 24(1), 45–52. doi:https://doi.org/10.1080/09715010.2017.1344572.