Plunging Flow Depth Estimation in a Stratified Dam Reservoir Using Neuro-Fuzzy Technique

Basit Öğe Kaydı
dc.authoridJoksimovic, Darko/0000-0001-7977-0566
dc.authoridKisi, Ozgur/0000-0001-7847-5872
dc.contributor.authorUnes, Fatih
dc.contributor.authorJoksimovic, Darko
dc.contributor.authorKisi, Ozgur
dc.date.accessioned2024-09-18T20:52:43Z
dc.date.available2024-09-18T20:52:43Z
dc.date.issued2015
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractThe cold river water inflow often plunges below the ambient dam reservoir water and becomes density underflow through the reservoir. The hydrodynamics of density currents and plunging are difficult to study in the natural environment and laboratory condition due to small-scale, entrainment and turbulent flows. Numerical modeling of plunging flow and defining of the plunging depth can provide valuable insights for the dam reservoir sedimentation and water quality problem. In this study, an adaptive neuro-fuzzy (NF) approach is proposed to estimate plunging flow depth in dam reservoir. The results of the NF model are compared with two-dimensional hydrodynamic model, artificial neural network (ANN), and multi linear regression (MLR) model results. The two-dimensional model is adapted to simulate density plunging flow simulation through a reservoir with sloping bottom. The model is developed using nonlinear and unsteady continuity, momentum, energy and k-epsilon turbulence model equations in the Cartesian coordinates. Density flow parameters such as velocity, plunging points, and plunging depths are determined from the simulation and model results. Mean square errors (MSE), mean absolute errors (MAE) and determination coefficient (R-2) statistics are used as comparing criteria for the evaluation of the models' performances. The NF model approach for the data yields the small MSE (1.18 cm), MAE (0.86 cm), and high determination coefficient (0.95-0.98). Based on the comparisons, it was found that the NF computing technique performs better than the other models in plunging flow depth estimation for the particular data sets used in this study.en_US
dc.identifier.doi10.1007/s11269-015-0978-y
dc.identifier.endpage3077en_US
dc.identifier.issn0920-4741
dc.identifier.issn1573-1650
dc.identifier.issue9en_US
dc.identifier.scopus2-s2.0-84929964664en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3055en_US
dc.identifier.urihttps://doi.org/10.1007/s11269-015-0978-y
dc.identifier.urihttps://hdl.handle.net/20.500.12483/11339
dc.identifier.volume29en_US
dc.identifier.wosWOS:000355266800003en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofWater Resources Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNeuro-fuzzyen_US
dc.subjectPlunging depthen_US
dc.subjectDam reservoiren_US
dc.subjectDensity flowen_US
dc.subjectMathematical modelen_US
dc.subjectNeural networken_US
dc.titlePlunging Flow Depth Estimation in a Stratified Dam Reservoir Using Neuro-Fuzzy Techniqueen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
[ N/A ]
İsim:
Tam Metin / Full Text
Boyut:
1.61 MB
Biçim:
Adobe Portable Document Format
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu