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Challenging soft computing optimization approaches in modeling complex hydraulic phenomenon of aeration process

Mahdavi-Meymand, Amin ; Scholz, Miklas LU and Zounemat-Kermani, Mohammad (2021) In ISH Journal of Hydraulic Engineering 27(sup1). p.58-69
Abstract


This study investigates and challenges the capability of standard and hybrid soft computing models of fuzzy c-means clustering adaptive neuro-fuzzy inference system (ANFIS), wavenet and artificial neural networks (MLPNN and RBFNN) to estimate the spillway aerator air demand in dams. For the learning process, four different meta-heuristic optimization methods (particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FA) and biogeography-based optimization (BBO)) are considered as alternatives to the classical optimization algorithms of the data-driven models. In addition to the data-driven models, the multiple linear regressions and some empirical... (More)


This study investigates and challenges the capability of standard and hybrid soft computing models of fuzzy c-means clustering adaptive neuro-fuzzy inference system (ANFIS), wavenet and artificial neural networks (MLPNN and RBFNN) to estimate the spillway aerator air demand in dams. For the learning process, four different meta-heuristic optimization methods (particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FA) and biogeography-based optimization (BBO)) are considered as alternatives to the classical optimization algorithms of the data-driven models. In addition to the data-driven models, the multiple linear regressions and some empirical relations are used to evaluate the performance of the models. Evaluation of the models is assessed with five different statistical parameters as well as the diagnostic tool of the Taylor’s diagram. Analysis of the models’ outcome reveals that the ANFIS-GA has the best performance associated with a standard root mean square error of 0.309 and a coefficient of determination (R
2
) of 0.93.

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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Aerator air flow, fuzzy inference systems, meta-heuristic algorithms, spillway aerator
in
ISH Journal of Hydraulic Engineering
volume
27
issue
sup1
pages
58 - 69
publisher
Taylor & Francis
external identifiers
  • scopus:85061430253
ISSN
2164-3040
DOI
10.1080/09715010.2019.1574619
language
English
LU publication?
yes
id
891c83a4-eb0c-44ba-b21e-f897ba9a9120
date added to LUP
2019-02-22 10:14:17
date last changed
2022-04-25 21:21:15
@article{891c83a4-eb0c-44ba-b21e-f897ba9a9120,
  abstract     = {{<p><br>
                                                         This study investigates and challenges the capability of standard and hybrid soft computing models of fuzzy c-means clustering adaptive neuro-fuzzy inference system (ANFIS), wavenet and artificial neural networks (MLPNN and RBFNN) to estimate the spillway aerator air demand in dams. For the learning process, four different meta-heuristic optimization methods (particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FA) and biogeography-based optimization (BBO)) are considered as alternatives to the classical optimization algorithms of the data-driven models. In addition to the data-driven models, the multiple linear regressions and some empirical relations are used to evaluate the performance of the models. Evaluation of the models is assessed with five different statistical parameters as well as the diagnostic tool of the Taylor’s diagram. Analysis of the models’ outcome reveals that the ANFIS-GA has the best performance associated with a standard root mean square error of 0.309 and a coefficient of determination (R                             <br>
                            <sup>2</sup><br>
                                                         ) of 0.93.                         <br>
                        </p>}},
  author       = {{Mahdavi-Meymand, Amin and Scholz, Miklas and Zounemat-Kermani, Mohammad}},
  issn         = {{2164-3040}},
  keywords     = {{Aerator air flow; fuzzy inference systems; meta-heuristic algorithms; spillway aerator}},
  language     = {{eng}},
  number       = {{sup1}},
  pages        = {{58--69}},
  publisher    = {{Taylor & Francis}},
  series       = {{ISH Journal of Hydraulic Engineering}},
  title        = {{Challenging soft computing optimization approaches in modeling complex hydraulic phenomenon of aeration process}},
  url          = {{http://dx.doi.org/10.1080/09715010.2019.1574619}},
  doi          = {{10.1080/09715010.2019.1574619}},
  volume       = {{27}},
  year         = {{2021}},
}