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Challenging soft co...
Challenging soft computing optimization approaches in modeling complex hydraulic phenomenon of aeration process
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- Mahdavi-Meymand, Amin (author)
- Shahid Bahonar University of Kerman
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- Scholz, Miklas (author)
- Lund University,Lunds universitet,Avdelningen för Teknisk vattenresurslära,Institutionen för bygg- och miljöteknologi,Institutioner vid LTH,Lunds Tekniska Högskola,Division of Water Resources Engineering,Department of Building and Environmental Technology,Departments at LTH,Faculty of Engineering, LTH,University of Johannesburg,University of Salford
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- Zounemat-Kermani, Mohammad (author)
- Shahid Bahonar University of Kerman
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(creator_code:org_t)
- 2019-02-11
- 2021
- English.
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In: ISH Journal of Hydraulic Engineering. - : Informa UK Limited. - 2164-3040 .- 0971-5010. ; 27:sup1, s. 58-69
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Vattenteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Water Engineering (hsv//eng)
Keyword
- Aerator air flow
- fuzzy inference systems
- meta-heuristic algorithms
- spillway aerator
Publication and Content Type
- art (subject category)
- ref (subject category)
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