Paper Info
Title
A combined adaptive neuro-fuzzy inference system-firefly algorithm model for predicting the roller length of a hydraulic jump on a rough channel bed.
Abstract
Hydraulic jumps can occur downstream of hydraulic structures, such as normal weirs, gates and ogee spillways. The roller length is one of the most important parameters of hydraulic jumps in open channels. In this study, the roller length of a hydraulic jump on a rough bed is predicted using a hybrid of adaptive neuro-fuzzy inference systems and the firefly algorithm (ANFIS–FA). First, the effect of parameters including the Froude number (Fr), sequent depth (h 2/h 1) and relative roughness (ks/h 1) upstream of a hydraulic jump is studied. Following the modeling result analysis, ANFIS–FA is introduced as the superior model for estimating the roller length of a hydraulic jump on a rough bed according to Fr, h 2/h 1 and ks/h 1. The calculated MAPE, RMSE and correlation coefficient values for the superior model are 7.606, 1.771 and 0.970, respectively. ANFIS–FA predicted approximately 40 % of the results with less than 5 % error, and only 36 % of data had more than 10 % error.
Year
DOI
Venue
2018
10.1007/s00521-016-2560-9
Neural Computing and Applications
Keywords
Field
DocType
Supercritical, Roller length, Jump, ANFIS, Firefly algorithm
Correlation coefficient,Mathematical optimization,Hydraulic structure,Control theory,Momentum-depth relationship in a rectangular channel,Mathematical analysis,Firefly algorithm,Hydraulic jump,Adaptive neuro fuzzy inference system,Jump,Mathematics,Froude number
Journal
Volume
Issue
ISSN
29
6
1433-3058
Citations 
PageRank 
References 
4
0.46
8
Authors
4
Name
Order
Citations
PageRank
Hamed Azimi1141.41
Hossein Bonakdari25811.71
Isa Ebtehaj3295.10
David G. Michelson411617.07