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Adaptive neuro-fuzzy selection of the optimal parameters of protective spur dike |
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| Authors: | Hossein Basser Shahaboddin Shamshirband Hojat Karami Dalibor Petkovi? Shatirah Akib Afshin Jahangirzadeh |
| Institution: | 1. Department of Civil Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia 2. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia 3. Department of Civil Engineering, Semnan University, Semnan, Iran 4. Department for Mechatronics and Control, Faculty of Mechanical Engineering, University of Ni?, Aleksandra Medvedeva 14, 18000, Nis, Serbia |
| Abstract: | This study proposes a new approach for determining optimum dimensions of protective spur dike to mitigate scour amount around existing spur dikes. The main objective of this article was to predict the most optimum values of the protective spur dikes to reach the best performance. To predict the protective spur dike parameters for scour controlling around spur dikes, this paper constructed a process which selects the optimal protective spur dike parameters in regard to actual length of the protective spur dike, actual length of the main spur dikes, distance between the protective spur dike and the first spur dike, angle between protective spur dike and flow direction, flow intensity and median size of bed sediments with adaptive neuro-fuzzy (ANFIS) method. To build a protective spur dike with the best features, it is desirable to select and analyze factors that are truly relevant or the most influential to the spur dike. This procedure is typically called variable selection, and it corresponds to finding a subset of the full set of recorded variables that exhibits good predictive abilities. In this study, architecture for modeling complex systems in function approximation and regression was used, based on using ANFIS. Variable searching using the ANFIS network was performed to determine how the five factors affect the protective spur dike. Experimental model of the protective spur dike was used to generate training and checking data for the ANFIS network. |
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