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

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 affecti...

Full description

Saved in:
Bibliographic Details
Published in:Alexandria engineering journal 2018-09, Vol.57 (3), p.1773-1782
Main Authors: Haghiabi, Amir Hamzeh, Parsaie, Abbas, Ememgholizadeh, Samad
Format: Article
Language:English
Subjects:
ANFIS
ANNs
Discharge coefficient
Gamma Test
Labyrinth weir
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1110-0168
DOI:10.1016/j.aej.2017.05.005