توسعه مدل‌های هیبریدی شبکه عصبی مصنوعی- الگوریتم تکاملی جهت پیش‌بینی عدد فرود جریان در کانال‌های باز در مدل‌سازی انتقال رسوب

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران

2 کارشناس ارشد، گروه مهندسی آب، پردیس ابوریحان، دانشگاه تهران، تهران، ایران

3 استادیار، گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه شهید مدنی آذربایجان، تبریز، ایران

10.22034/jewe.2020.248209.1421

چکیده

نویسندگان این مقاله متفقاً تمایل داشتند به خاطر وجود ایرادات متعدد و عدم رعایت مالکیت معنوی داده های مورد استفاده، مقاله را باز پس گیری نمایند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Development of a Hybrid ANN-evolutionary Algorithms Models to Predict the Froude Number in Open Channel Flows in Modeling of Sediment Transport

نویسندگان [English]

  • Naser Arya azar 1
  • Sami Ghordoyee Milan 2
  • Nazila Kardan 3
1 M. Sc., Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
2 M. Sc., Department of Irrigation and Drainage Engineering, Aburaihan Campus, University of Tehran, Tehran, Iran
3 Assist. Professor, Department of Civil Engineering, Azarabaijan Shahid Madani University, Tabriz, Iran
چکیده [English]

The authors unanimously wish to retract this paper because of several incorrect statements and erroneous presentation and copyrights issues of primary data.

کلیدواژه‌ها [English]

  • ANN Hybrid Model
  • ANN
  • Froude number
  • Sediment transport
Ab Ghani, A. (1993). Sediment Transport in Sewers. PhD Thesis, University of Newcastle upon Tyne, UK.
Arumugam, M. S., Rao, M. V. C. and Chandramohan, A. (2008). A new and improved version of particle swarm optimization algorithm with global–local best parameters. Knowl. Inf. Syst., 16(3), 331-357.
Bong, C. H. J. (2013). Self-cleansing Urban Drain Using Sediment Flushing Gate Based on Incipient Motion. Ph.D. Thesis. Universiti Sains Malaysia.
Bong, C. H. J., Lau, T. L., Ab Ghani, A. and Chan, N. W. (2016). Sediment deposit thickness and its effect on critical velocity for incipient motion. Water Sci. Technol. 74(8), 1876-1884.
Butler, D., May, R. and Ackers, J. (2003). Self-cleansing sewer design based on sediment transport principles. J. Hydraul. Eng., ASCE, 129(4), 276–282.
Chen, X. Y. and Chau, K. W. (2016). A hybrid double feedforward neural network for suspended sediment load estimation. Water Resour Manag., 30(7), 2179–2194.
Chau, K. W. (2006). Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. J. Hydrol., 329(3-4), 363-367.
Delleur, J. W. (2001). New results and research needs on sediment movement in urban drainage. J. Water Resour. Plan. Manag., 127(3), 186-193.
Eberhart, R. and Kennedy, J. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. 4, pp. 1942-1948). Citeseer.
El-Zaemey, A. K. S. (1991). Sediment Transport Over Deposited Beds in Sewers. PhD Thesis, University of Newcastle upon Tyne, UK.
Garoosi Nejhad, A. and Bozorg Hadad, O. (2011). Optimal operation of the tank using algorithm implementation Optimization of firefly. Fifth Iranian Water Resources Management Conference, Shahid Beheshti University, Iran [In Persian].
Haykin, S. (1999). Neural network a comprehensive foundation. Prentice-Hall, New Jersey.
Hosseini-Moghari, S. and Banihabib, M. (2014). Optimizing operation of reservoir for agricultural water supply using firefly algorithm. J. Soil Water Conserv., 3(4), 17-31 [In Persian].
Kennedy, J. and Eberhart, R. (1995). Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948.
Kisi, O. and Shiri, J. (2012). River suspended sediment estimation by climatic variables implication: Comparative study among soft computing techniques. Comput. Geosci., 43, 73-82.
Lippman, R. (1987). An introduction to computing with neural nets. IEEE ASSP Mag., 4(2), 4-22.
May, R. (1993). Sediment Transport in Pipes and Sewers with Deposited Beds. Report SR 320, HR Wallingford, Oxfordshire, UK.
Mayerle, R., Nalluri, C. and Novak, P. (1991). Sediment transport in rigid bed conveyances. J. Hydraul. Res., 29(4), 475-495.
Mayerle, R. (1988). Sediment Transport in Rigid Boundary Channels. PhD thesis, University of Newcastle upon Tyne, Newcastle upon Tyne, UK.
Mehra, P. and Wah, B. W. (1990). Artificial neural networks: concepts and theory. IEEE Computer Society Press.
Montes, C., Vanegas, S., Kapelan Z., Berardi, L. and Saldarriaga, J. (2020). Non-deposition self-cleansing models for large sewer pipes. Water Sci. Technol., 81(3), 606-621.
Novak, P. and Nalluri, C. (1975). Sediment transport in smooth fixed bed channels. J. Hydraul. Div., 101(9), 1139-1154.
Olyaie, E., Banejad, H., Chau, K. W. and Melesse, A. M. (2015). A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States. Environ. Monit. Assess., 87(4), 189.
Ota, J. J. and Perrusquia, G. S. (2013). Particle velocity and sediment transport at the limit of deposition in sewers. Water Sci. Technol., 67(5), 959–967.
Piotrowski, A. P. and Napiorkowski, J. J. (2011). Optimizing neural networks for river flow forecasting–Evolutionary Computation methods versus the Levenberg–Marquardt approach. J. Hydrol., 407(1-4), 12-27.
Safari, M. J. S., Ebtehaj, I., Bonkdari, H. and Es-haghi, M. S. (2019). Sediment transport modeling in rigid boundary open channels using generalize structure of group method of data handling. J. Hydrol., 577, 125-142.
Safari, M. J. S. and Danandeh Mehr, A. (2018). Multigene genetic programming for sediment transport modeling in sewers at non-deposition with deposited bed condition. Int. J. Sediment Res., 33(3), 262–270.
Salem, A. M. (2013). The effects of the sediment bed thickness on the incipient motion of particles in a rigid rectangular channel. Seventeenth International Water Technology Conference (IWTC17), Istanbul, Turkey.
Storn, R. and Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. J. Global Optim., 11, 341–359.
Vongvisessomjai, N., Tingsanchali, T. and Babel, M. S. (2010). Non-deposition design criteria for sewers with part-full flow. Urban Water J., 7(1), 61–77.
Yang, X-S. (2010). Firefly algorithm, stochastic test functions and design optimization. Int. J. Bio-Inspired Comput., 2(2), 78–84.
Zhang, Y. (2006). Towards piecewise-linear primal neural networks for optimization and redundant robotics. In: Proceedings of IEEE International Conference on Networking, Sensing and Control, pp. 374–379. IEEE Computer Society Press, Los Alamitos.
Zhu Y. M., Lu X. X. and Zhou Y. (2007). Suspended sediment flux modeling with artificial neural network: An example of the Long Chuanjiang River in the Upper Yangtze Catchment, China. Geomorph., 84(1), 111-125.