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Adaptive neuro-fuzzy prediction of the optimum parameters of protective spur dike

Author

Listed:
  • Hossein Basser
  • Shahaboddin Shamshirband
  • Dalibor Petković
  • Hojat Karami
  • Shatirah Akib
  • Afshin Jahangirzadeh

Abstract

This study proposes a new approach for determining the optimum dimensions of a protective spur dike to mitigate the amount of scour around existing spur dikes. Several parameters of a protective spur dike were studied to determine their optimum values, including length, angle, and distance. Also the effect of changes of flow intensity and sediment size were examined. The main objective of this article was to predict the optimum values of protective spur dikes to attain the best performance. To predict the parameters of protective spur dikes for controlling the scour around spur dikes, we used the adaptive neuro-fuzzy inference system method to construct a process that simulates the optimal parameters of a protective spur dike, including the actual length of the protective spur dike, the actual length of the main spur dikes, the distance between the protective spur dike and the first spur dike, the angle between the protective spur dike and the direction of flow, the intensity of the flow, and median size of the bed sediments. This intelligent estimator was implemented using MATLAB/Simulink, and the performances were investigated. The simulation results presented in this paper show the effectiveness of the developed method. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Hossein Basser & Shahaboddin Shamshirband & Dalibor Petković & Hojat Karami & Shatirah Akib & Afshin Jahangirzadeh, 2014. "Adaptive neuro-fuzzy prediction of the optimum parameters of protective spur dike," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(3), pages 1439-1449, September.
    • Handle: RePEc:spr:nathaz:v:73:y:2014:i:3:p:1439-1449
    DOI: 10.1007/s11069-014-1145-0
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    References listed on IDEAS

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    1. Tuan Thieu Quang & Hocine Oumeraci, 2012. "Numerical modelling of wave overtopping-induced erosion of grassed inner sea-dike slopes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 417-447, September.
    2. Jie Chen & Zhenhua Huang & Changbo Jiang & Bin Deng & Yuannan Long, 2013. "Tsunami-induced scour at coastal roadways: a laboratory study," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 69(1), pages 655-674, October.
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    Cited by:

    1. Hossein Basser & Shahaboddin Shamshirband & Dalibor Petković & Hojat Karami & Shatirah Akib & Afshin Jahangirzadeh, 2020. "Retraction Note to: Adaptive neuro-fuzzy prediction of the optimum parameters of protective spur dike," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(2), pages 2629-2630, September.
    2. Shamshirband, Shahaboddin & Petković, Dalibor & Anuar, Nor Badrul & Gani, Abdullah, 2014. "Adaptive neuro-fuzzy generalization of wind turbine wake added turbulence models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 270-276.
    3. Omid Ghorbanzadeh & Hashem Rostamzadeh & Thomas Blaschke & Khalil Gholaminia & Jagannath Aryal, 2018. "A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(2), pages 497-517, November.
    4. Naji, Sareh & Shamshirband, Shahaboddin & Basser, Hossein & Keivani, Afram & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Petković, Dalibor, 2016. "Application of adaptive neuro-fuzzy methodology for estimating building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1520-1528.

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