Combination of neuro-fuzzy network and genetic algorithm for estimating discharge capacity of triangular in plan weirs
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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JR_ARWW-8-1_001
تاریخ نمایه سازی: 15 شهریور 1400
Abstract:
In the current study, a new hybrid of the genetic algorithm (GA) and adaptive Neuro-fuzzy inference system (ANFIS) was introduced to model the discharge coefficient (DC) of triangular weirs. The genetic algorithm was implemented for increasing the efficiency of ANFIS by adjusting membership functions as well as minimizing error values. To evaluate the proficiency of the proposed hybrid method, the Monte Carlo simulations (MCS) and the k-fold validation method (k=۵) was applied. The results of developed hybrid model indicate that the weir vortex angle, flow Froude number, the ratio of the weir length to its height, the ratio of the channel width to the weir length and ratio of the flow head to the weir height are the most effective parameters in the DC estimation. The quantitative examination of the proposed hybrid method indicates that the Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) are as ۰.۰۱۶ and ۱.۶۴۷ (respectively) for the superior model. Besides, the Froude number is found as the most effective variable in DC modeling through the quantitative analysis. A comparison of the developed hybrid ANFIS-GA with the individual ANFIS model in the DC estimation indicates the hybrid model outperformed than the individual one.
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Authors
Ali Azizpor
Department of Water Engineering, Faculty of Agriculture, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
Ahmad Rajabi
Department of Water Engineering, Faculty of Agriculture, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
Fariborz Yosefvand
Department of Water Engineering, Faculty of Agriculture, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
Saeid Shabanlou
Department of Water Engineering, Faculty of Agriculture, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
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