Using a hybrid artificial intelligence approach to estimate length of the hydraulic jump caused by novel kind of Sharp-Crested V-notch weirs

References

• Ahmed, H. M. A., El Gendy, M., Mirdan, A. M. H., Ali, A. A. M., & Haleem, F. S. F. A. (2014). Effect of corrugated beds on characteristics of submerged hydraulic jump. Ain Shams Engineering Journal, 5(4), 1033–1042. https://doi.org/10.1016/j.asej.2014.06.006
   Google Scholar
• Azimi, H., Bonakdari, H., Ebtehaj, I., Gharabaghi, B., & Khoshbin, F. (2018). Evolutionary design of generalized group method of data handling-type neural network for estimating the hydraulic jump roller length. Acta Mechanica, 229(3), 1197–1214. https://doi.org/10.1007/s00707-017-2043-9
   Web of Science ®Google Scholar
• Bayon, A., Valero, D., García-Bartual, R., Jose Valles-Moran, F., & Lopez-Jimenez, P. A. (2016). Performance assessment of OpenFOAM and FLOW-3D in the numerical modeling of a low Reynolds number hydraulic jump. Environmental Modelling & Software, 80, 322–335. https://doi.org/10.1016/j.envsoft.2016.02.018
   Web of Science ®Google Scholar
• Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford University Press.
   Google Scholar
• Boss, M. G. (1998). Discharge measurement structures. International Institute for Land Reclamation and Improvement (ILRI).
   Google Scholar
• Bradley, J. N., & Peterka, A. J. (1957). The hydraulic design of stilling basins: Hydraulic jumps on a horizontal apron (basin I). Journal of the Hydraulics Division, 83(5), 1–24. https://doi.org/10.1061/JYCEAJ.0000126
   Google Scholar
• Brereton, R. G., & Lloyd, G. R. (2010). Support vector machines for classification and regression. The Analyst, 135(2), 230–267. https://doi.org/10.1039/b918972f
   PubMed Web of Science ®Google Scholar
• Chandel, A. and Shankar, V. (2021). Evaluation of empirical relationships to estimate the hydraulic conductivity of borehole soil samples. ISH Journal of Hydraulic Engineering, pp. 1–10.
   Google Scholar
• Chanson, H. (2007). Bubbly flow structure in hydraulic jump. European Journal of Mechanics - B/Fluids, 26(3), 367–384. https://doi.org/10.1016/j.euromechflu.2006.08.001
   Web of Science ®Google Scholar
• Chanson, H., & Brattberg, T. (2000). Experimental study of the air–water shear flow in a hydraulic jump. International Journal of Multiphase Flow, 26(4), 583–607. https://doi.org/10.1016/S0301-9322(99)00016-6
   Web of Science ®Google Scholar
• Das, A. (2007). Solution of specific energy and specific force equations. Journal of Irrigation and Drainage Engineering, 133(4), 407–410. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(407)
   Web of Science ®Google Scholar
• Dimitri, P., Solomatine, D., & Ostfeld, A. (2008). Data-driven modelling: Some past experiences and new approaches. Journal of Hydroinformatics, 10(1), 3–22. https://doi.org/10.2166/hydro.2008.015
   Web of Science ®Google Scholar
• Dorigo, M., Caro, G. D., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life, 5(2), 137–172. https://doi.org/10.1162/106454699568728
   PubMed Web of Science ®Google Scholar
• Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 26(1), 29–41. https://doi.org/10.1109/3477.484436
   PubMed Web of Science ®Google Scholar
• Elbaz, K., Shen, S.-L., Sun, W.-J., Yin, Z.-Y., & Zhou, A. (2020). Prediction model of shield performance during tunneling via incorporating improved particle swarm optimization into ANFIS. IEEE Access, 8, 39659–39671. https://doi.org/10.1109/ACCESS.2020.2974058
   Google Scholar
• Ferreira, C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. Complex System, 13, 87–129.
   Google Scholar
• Gao, Y., Guan, H., Qi, Z., Hou, Y., & Liu, L. (2013). A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. Journal of Computer and System Sciences, 79(8), 1230–1242. https://doi.org/10.1016/j.jcss.2013.02.004
   Web of Science ®Google Scholar
• Gupta, S. K., Mehta, R. C., & Dwivedi, V. K. (2013). Modeling of relative length and relative energy loss of free hydraulic jump in horizontal prismatic channel. Procedia Engineering, 51, 529–537. https://doi.org/10.1016/j.proeng.2013.01.075
   Google Scholar
• Hager, W. H. (1992a). Discussion of design procedure for flow over side weirs. Journal of Irrigation and Drainage Engineering, 118(2), 340–340. https://doi.org/10.1061/(ASCE)0733-9437(1992)118:2(340.2)
   Web of Science ®Google Scholar
• Hager, W. H. (1992b). Discussion of force on slab beneath hydraulic jump. Journal of Hydraulic Engineering, 118(4), 666–667. https://doi.org/10.1061/(ASCE)0733-9429(1992)118:4(666)
   Web of Science ®Google Scholar
• Houichi, L., Dechemi, N., Heddam, S., & Achour, B. (2013). An evaluation of ANN methods for estimating the lengths of hydraulic jumps in U-shaped channel. Journal of Hydroinformatics, 15(1), 147–154. https://doi.org/10.2166/hydro.2012.138
   Web of Science ®Google Scholar
• Karami, H., Karimi, S., Rahmanimanesh, M., & Farzin, S. (2016a). Predicting discharge coefficient of triangular labyrinth weir using support vector regression, support vector regression-firefly, response surface methodology and principal component analysis. Flow Measurement and Instrumentation, 55, 75–81. https://doi.org/10.1016/j.flowmeasinst.2016.11.010
   Web of Science ®Google Scholar
• Khosravinia, P., Sanikhani, H., & Abdi, C. (2017). Application of soft computing techniques to Predict of hydraulic jump length on rough beds. Journal of Rehabilitation in Civil Engineering, 6(2),139–153. https://doi.org/10.22075/jRCE.2017.11047.1180
   Google Scholar
• Kisi, O. (2007). Stream flow forecasting using different artificial neural network algorithms. jHydrolEng ASCE, 12(5), 532–539.
   Web of Science ®Google Scholar
• Lawrence, S., Back, A. D., Tsoi, A. C., & Giles, C. L. (1997). On the distribution of performance from multiple neural-network trials. IEEE Transactions on Neural Networks, 8(6), 1507–1517. https://doi.org/10.1109/72.641472
   PubMed Web of Science ®Google Scholar
• Lin, C., Wu, G., Xia, F., Li, M., Yao, L., & Pei, Z. (2012). Energy efficient ant colony algorithms for data aggregation in wireless sensor networks. Journal of Computer and System Sciences, 78(6), 1686–1702. https://doi.org/10.1016/j.jcss.2011.10.017
   Web of Science ®Google Scholar
• Lin, S.-S., Shen, S.-L., Zhou, A., & Xu, Y.-S. (2021). Novel model for risk identification during karst excavation. Reliability Engineering & System Safety, 209, 107435. https://doi.org/10.1016/j.ress.2021.107435
   Web of Science ®Google Scholar
• Maniezzo, V., & Colorni, A. (1999). The ant system applied to the quadratic assignment problem. IEEE Transactions on Knowledge and Data Engineering, 5(1999), 769–778.
   Google Scholar
• Momeni, E., Nazir, R., Jahed Armaghani, D., & Maizir, H. (2014). Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement, 57, 122–131. https://doi.org/10.1016/j.measurement.2014.08.007
   Web of Science ®Google Scholar
• Murzyn, F., & Chanson, H. (2009). Two-phase gas–liquid flow properties in the hydraulic jump: Review and perspectives. In S. Martin & J. R. Williams (Eds.), Multiphase flow research (pp. 497–542). Nova Science Publishers.
   Google Scholar
• Naseri, M., & Othman, F. (2012). Determination of the length of hydraulic jumps using artificial neural networks. Advances in Engineering Software, 48, 27–31. https://doi.org/10.1016/j.advengsoft.2012.01.003
   Web of Science ®Google Scholar
• Olatomiwa, L., Mekhilef, S., Shamshirband, S., Mohammadi, K., Petković, D., & Sudheer, C. (2015). A support vector machine–firefly algorithm-based model for global solar radiation prediction. Solar Energy, 115, 632–644. https://doi.org/10.1016/j.solener.2015.03.015
   Web of Science ®Google Scholar
• Omid, M. H., Omid, M., & Esmaeeli Varaki, M. (2005). Modelling hydraulic jumps with artificial neural networks. In Proceedings of the Institution of Civil Engineers-Water Management (Vol. 158, No. 2, pp. 65–70). Thomas Telford Ltd. https://doi.org/10.1680/wama.2005.158.2.65
   Google Scholar
• Pai, P. F., & Hong, W. C. (2007). A recurrent support vector regression model in rainfall forecasting. Hydrological Processes, 21(6), 819–827. https://doi.org/10.1002/hyp.6323
   Web of Science ®Google Scholar
• Parrella, F. (2007). Online support vector regression [Master's Thesis]. Department of Information Science, University of Genoa. p. 93.
   Google Scholar
• Rashwan, I. M. H. (2013). Analytical solution to problems of hydraulic jump in horizontal triangular channels. Ain Shams Engineering Journal, 4(3), 365–368. https://doi.org/10.1016/j.asej.2012.11.007
   Google Scholar
• Saadatnejadgharahassanlou, H., Ilkhanipour Zeynali, R., Vaheddoost, B., & Gharehbaghi, A. (2020). Parametric and nonparametric regression models in study of the length of hydraulic jump after a multi-segment sharp-crested V-notch weir. Water Supply, 20(3), 809–818. https://doi.org/10.2166/ws.2019.198
   Google Scholar
• Safari, M. J. S., Aksoy, H., & Mohammadi, M. (2016). Artificial neural network and regression models for flow velocity at sediment incipient deposition. Journal of Hydrology, 541, 1420–1429. https://doi.org/10.1016/j.jhydrol.2016.08.045
   Web of Science ®Google Scholar
• Shiri, J., Sadraddini, A. A., Nazemi, A. H., Kisi, O., Landeras, G., Fakheri Fard, A., & Marti, P. (2014). Generalizability of gene expression programming-based approaches for estimating daily reference evapotranspiration in coastal stations of Iran. Journal of Hydrology, 508, 1–11. https://doi.org/10.1016/j.jhydrol.2013.10.034
   Web of Science ®Google Scholar
• Silvester, R. (1965). Theory and experiment on the hydraulic jump. In Proceedings of the 2nd Australasian conference on hydraulics and fluid mechanics (pp. A25–A39). University of Auckland.
   Google Scholar
• Smola, A. J., & Scholkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
   Web of Science ®Google Scholar
• Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.
   Google Scholar
• United States Bureau of Reclamation (USBR). (2016). https://www.usbr.gov/
   Google Scholar
• Vaheddoost, B., Aksoy, H., & Abghari, H. (2016). Prediction of water level using monthly lagged data in Lake Urmia, Iran. Water Resources Management, 30(13), 4951–4967. https://doi.org/10.1007/s11269-016-1463-y
   Web of Science ®Google Scholar
• Valiani, A. (1997). Linear and angular momentum conservation in hydraulic jump. Journal of Hydraulic Research, 35(3), 323–354. https://doi.org/10.1080/00221689709498416
   Web of Science ®Google Scholar
• Vapnik, V., & Chervonenkis, A. (1974). Theory of pattern recognition. Nauka.
   Google Scholar
• Vatankhah, A. R., & Kouchakzadeh, S. (2008). Discussion of solution of specific energy and specific force equations. Journal of Irrigation and Drainage Engineering, 134(6), 880–882. https://doi.org/10.1061/(ASCE)0733-9437(2008)134:6(880)
   Web of Science ®Google Scholar
• Wang, H., & Chanson, H. (2015). Experimental study of turbulent fluctuations in hydraulic jumps. Journal of Hydraulic Engineering, 141(7), 04015010. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001010
   Web of Science ®Google Scholar
• Wang, S., Fu, Z. Y., Chen, H. S., Nie, Y. P., & Wang, K. L. (2016). Modeling daily reference ET in the Karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN). Theoretical and Applied Climatology, 126, 493–504. https://doi.org/10.1007/s00704-015-1602-z.
   Web of Science ®Google Scholar
• Yu, P. S., Chen, S. T., & Chang, I. F. (2006). Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328(3–4), 704–716. nohttps://doi.org/10.1016/j.jhydrol.2006.01.021
   Web of Science ®Google Scholar
• Yu, X., Zhang, X., & Qin, H. (2018). A data-driven model based on Fourier transform and support vector regression for monthly reservoir inflow forecasting. Journal of Hydro-Environment Research, 18, 12–24. https://doi.org/10.1016/j.jher.2017.10.005
   Web of Science ®Google Scholar
• Zaji, A. H., Bonakdari, H., Khodashenas, S. R., & Shamshirband, S. (2016). Firefly optimization algorithm effect on support vector regression prediction improvement of a modified labyrinth side weir’s discharge coefficient. Journal of Applied Mathematics and Computing, 274, 14–19.
   Google Scholar
• Zarei, S., Yosefvand, F., & Shabanlou, S. (2019). Discharge coefficient of side weirs on converging channels using extreme learning machine modeling method. Measurement, 152, 107321. https://doi.org/10.1016/j.measurement.2019.107321
   Web of Science ®Google Scholar
• Zhang, N., Shen, S.-L., Zhou, A., & Jin, Y.-F. (2021). Application of LSTM approach for modelling stress–strain behaviour of soil. Applied Soft Computing, 100, 106959. https://doi.org/10.1016/j.asoc.2020.106959
   Web of Science ®Google Scholar
• Zhang, P., Li, H., Ha, Q. P., Yin, Z.-Y., & Chen, R. P. (2020a). Reinforcement learning based optimizer for improvement of tunneling-induced settlement prediction model. Advanced Engineering Informatics, 45, 101097. https://doi.org/10.1016/j.aei.2020.101097
   Web of Science ®Google Scholar
• Zhang, P., Yin, Z.-Y., Jin, Y. F., & Ye, G. L. (2020b). An AI-based model for describing cyclic characteristics of granular materials. International Journal for Numerical and Analytical Methods in Geomechanics, 44(9), 1315–1335. https://doi.org/10.1002/nag.3063
   Web of Science ®Google Scholar
• Zhang, P., Yin, Z.-Y., Zheng, Y., & Gao, F. P. (2020c). A LSTM surrogate modelling approach for caisson foundations. Ocean Engineering, 204, 107263. https://doi.org/10.1016/j.oceaneng.2020.107263
   Web of Science ®Google Scholar