Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System | Zendy

Amir Hamzeh Haghiabi | Zendy; Abbas Parsaie | Zendy; Samad Ememgholizadeh | Zendy

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Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System

Author(s) -

Amir Hamzeh Haghiabi,

Abbas Parsaie,

Samad Ememgholizadeh

Publication year - 2017

Publication title -

alexandria engineering journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.584

H-Index - 58

eISSN - 2090-2670

pISSN - 1110-0168

DOI - 10.1016/j.aej.2017.05.005

Subject(s) - adaptive neuro fuzzy inference system , weir , discharge coefficient , mean squared error , perceptron , mathematics , coefficient of determination , artificial neural network , statistics , control theory (sociology) , engineering , computer science , fuzzy logic , fuzzy control system , artificial intelligence , mechanical engineering , cartography , control (management) , geography , nozzle

In this paper, the discharge coefficient of triangular labyrinth weir was predicted using multi-layer perceptron (MLP) neural network and Adaptive Neuro Fuzzy Inference System (ANFIS). To this purpose, 223 related dataset were collected. The Gamma Test (GT) was carried out to obtain the most affective parameters on the discharge coefficient. The results of the GT indicated that the ratio of length of crest of weir to the main channel width Lw/Wmc, the ratio of length of one cycle to its width (Lc/Wc) and the ratio of total upstream head flow to the weir height H/P are the most important parameters. With regarding to the results of the GT, the structure of ANFIS model was designed. The results of ANFIS model with error indices including coefficient of determination value of 0.97 and root mean square error value of 0.03 was so suitable. Comparison the results of MLP with ANFIS model showed that both models has so suitable performance however the structure of ANFIS model is more optimal.

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