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Research Articles

Prediction models for flow resistance in flexible vegetated channels

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Pages 427-437 | Received 01 Apr 2017, Accepted 18 Dec 2017, Published online: 07 Mar 2018

References

  • Abdul Ghaffar, A., et al., 2004. Determining Manning’s flow resistance coefficient for rivers in Malaysia. 1st International Conference on Managing Rivers in the 21st Century: Issues and Challenges, Penang, Malaysia.
  • Ab. Ghani, A. and Md. Azamathulla, H., 2010. Gene-expression programming for sediment transport in sewer pipe systems. Journal of Pipeline Systems Engineering and Practice, 2 (3), 102–106. doi: 10.1061/(ASCE)PS.1949-1204.0000076
  • Ab. Ghani, A., et al., 2004. Bio-Ecological Drainage System (BIOECODS): Concept, Design and Construction. lnternariond Conference on HydroScience and Engineering. Brisbane, Australia.
  • Antoniou, M.A., et al., 2010. A gene expression programming environment for fatigue modeling of composite materials. In: S. Konstantopoulos, et al., eds. Artificial intelligence: theories, models and applications. Berlin: Springer, 297–302.
  • Chen, Y.-C., et al., 2009. Retardance coefficient of vegetated channels estimated by the Froude number. Ecological Engineering, 35 (7), 1027–1035. doi: 10.1016/j.ecoleng.2009.03.002
  • Chow, V.T., 1959. Open channel hydraulics. New York: McGraw-Hill.
  • Cramer, N.L., 1985. A representation for the adaptive generation of simple sequential programs. Proceedings of the First International Conference on Genetic Algorithms.
  • Duan, L., et al., 2006. Distance guided classification with gene expression programming. In: X. Li, O.R. Zaïane, and Z. Li, eds. Advanced data mining and applications. Berlin: Springer, 239–246.
  • Ebtehaj, I., et al., 2015. Gene expression programming to predict the discharge coefficient in rectangular side weirs. Applied Soft Computing, 35, 618–628. doi: 10.1016/j.asoc.2015.07.003
  • Edossa, D.C. and Babel, M.S., 2011. Application of ANN-based streamflow forecasting model for agricultural water management in the Awash River Basin, Ethiopia. Water Resources Management, 25 (6), 1759–1773. doi: 10.1007/s11269-010-9773-y
  • Escarameia, M., Gasowski, Y., and May, R., 2002. Grassed drainage channels – hydraullic resistance characteristic. Proceedings of the ICE-Water and Maritime Engineering, 154 (4), 333–341. doi: 10.1680/wame.2002.154.4.333
  • Fernando, D.A. and Shamseldin, A.Y., 2009. Investigation of internal functioning of the radial-basis-function neural network river flow forecasting models. Journal of Hydrologic Engineering, 14 (3), 286–292. doi: 10.1061/(ASCE)1084-0699(2009)14:3(286)
  • Ferreira, C., 2001. Algorithm for solving gene expression programming: a new adaptive problems. Complex Systems, 13 (2), 87–129.
  • Ferreira, C., 2002. Gene expression programming in problem solving. In: R. Roy, et al., eds. Soft computing and industry. London: Springer, 635–653.
  • Guven, A. and Gunal, M., 2008. Genetic programming approach for prediction of local scour downstream of hydraulic structures. Journal of Irrigation and Drainage Engineering, 134 (2), 241–249. doi: 10.1061/(ASCE)0733-9437(2008)134:2(241)
  • Hagan, M.T. and Menhaj, M.B., 1994. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5 (6), 989–993. doi: 10.1109/72.329697
  • Hager, W.H. and Liiv, U., 2008. Johann Nikuradse–Hydraulic experimenter. Journal of Hydraulic Research, 46 (4), 435–444.
  • J̧drzejowicz, P. and Ratajczak-Ropel, E., 2009. Agent-based gene expression programming for solving the RCPSP/max problem. In: M. Kolehmainen, P. Toivanen, and B. Beliczynski, eds. Adaptive and natural computing algorithms. Berlin: Springer, 203–212.
  • Juma, I.A., Hussein, H.H., and Al-Sarraj, M.F., 2014. Analysis of hydraulic characteristics for hollow semi-circular weirs using artificial neural networks. Flow Measurement and Instrumentation, 38, 49–53. doi: 10.1016/j.flowmeasinst.2014.05.003
  • Kisi, O., Shiri, J., and Nikoofar, B., 2012a. Forecasting daily lake levels using artificial intelligence approaches. Computers & Geosciences, 41, 169–180. doi: 10.1016/j.cageo.2011.08.027
  • Kisi, O., et al., 2012b. Suspended sediment modeling using genetic programming and soft computing techniques. Journal of Hydrology, 450–451, 48–58. doi: 10.1016/j.jhydrol.2012.05.031
  • Kouwen, N., 1992. Modern approach to design of grassed channels. Journal of Irrigation and Drainage Engineering, 118 (5), 733–743. doi: 10.1061/(ASCE)0733-9437(1992)118:5(733)
  • Kouwen, N., Unny, T., and Hill, H.M., 1969. Flow retardance in vegetated channels. Journal of the Irrigation and Drainage Division, 95 (2), 329–344.
  • Koza, J.R., 1992. Genetic programming: on the programming of computers by means of natural selection. Cambridge, MA: MIT Press.
  • Ladson, A., et al., 2002. An Australian Handbook of Stream Roughness Coefficients. How hydrographers can help. Proceeding of 11th Australian Hydrographic conference. Sydney.
  • Lang, S., Ladson, T., and Anderson, B., 2004. A review of empirical equations for estimating stream roughness and their application to four streams in Victoria. Australian Journal of Water Resources, 8 (1), 69–82. doi: 10.1080/13241583.2004.11465245
  • Liu, X.-g. and Zeng, Y.-h., 2016. Drag coefficient for rigid vegetation in subcritical open channel. Procedia Engineering, 154, 1124–1131. doi: 10.1016/j.proeng.2016.07.522
  • Mohammadpour, R., et al., 2016. Prediction of water quality index in free surface constructed wetlands. Environmental Earth Sciences, 75 (2), 439. doi: 10.1007/s12665-015-4905-6
  • Mustafa, M., et al., 2012. River suspended sediment prediction using various multilayer perceptron neural network training algorithms – a case study in Malaysia. Water Resources Management, 26 (7), 1879–1897. doi: 10.1007/s11269-012-9992-5
  • Panagoulia, D., 2006. Artificial neural networks and high and low flows in various climate regimes. Hydrological Sciences Journal, 51 (4), 563–587. doi: 10.1623/hysj.51.4.563
  • Rahimi, M., et al., 2015. Application of artificial neural network and genetic algorithm approaches for prediction of flow characteristic in serpentine microchannels. Chemical Engineering Research and Design, 98, 147–156. doi: 10.1016/j.cherd.2015.05.005
  • Salmasi, F., et al., 2013. Predicting discharge coefficient of compound broad-crested weir by using genetic programming (GP) and artificial neural network (ANN) techniques. Arabian Journal of Geosciences, 6 (7), 2709–2717. doi: 10.1007/s12517-012-0540-7
  • Seckin, G., et al., 2009. Application of ANN techniques for estimating backwater through bridge constrictions in Mississippi River basin. Advances in Engineering Software, 40 (10), 1039–1046. doi: 10.1016/j.advengsoft.2009.03.002
  • Simons, D.B. and Şentürk, F., 1992. Sediment transport technology: water and sediment dynamics. Littleton, CO: Water Resources Publications.
  • Smith, J. and Eli, R.N., 1995. Neural-network models of rainfall-runoff process. Journal of Water Resources Planning and Management, 121 (6), 499–508. doi: 10.1061/(ASCE)0733-9496(1995)121:6(499)
  • SuDS, 2015. Sustainable Drainage Manual: Guidance on Design of Swales. Construction Industry Research and Information Association. (CIRIA). Part D Chapter 17.
  • Tawfik, M., Ibrahim, A., and Fahmy, H., 1997. Hysteresis sensitive neural network for modeling rating curves. Journal of Computing in Civil Engineering, 11 (3), 206–211. doi: 10.1061/(ASCE)0887-3801(1997)11:3(206)
  • WSUD, 2004. Water sensitive urban design, technical guidelines for Western Sydney. Section 5. North Sydney: URS Australia.
  • Yang, C.T, 1996. Sediment transport: theory and practice. New York: McGraw-Hill.
  • Yen, B.C., 2002. Open channel flow resistance. Journal of Hydraulic Engineering, 128 (1), 20–39. doi: 10.1061/(ASCE)0733-9429(2002)128:1(20)
  • Zahiri, A., Dehghani, A., and Azamathulla, H.M., 2015. Application of gene-expression programming in hydraulic engineering. In: A. Gandomi, A. Alavi, and C. Ryan, eds. Handbook of genetic programming applications. Cham: Springer, 71–97.
  • Zakaria, N.A., et al., 2003. Bio-ecological drainage system (BIOECODS) for water quantity and quality control. International Journal of River Basin Management, 1 (3), 237–251. doi: 10.1080/15715124.2003.9635210

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