Prediction of Density Flow Plunging Depth in Dam Reservoirs: An Artificial Neural Network Approach
Yükleniyor...
Dosyalar
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Experimental findings and observations indicate that plunging flow is related to the formation of bed load deposition in dam reservoirs. The sediment delta begins to form in the plunging region where the inflow river water meets the ambient reservoir water. Correct estimation of dam reservoir flow, plunging point, and plunging depth is crucial for dam reservoir sedimentation and water quality issues. In this study, artificial neural network (ANN), multi-linear regression (MLR), and two-dimensional hydrodynamic model approaches are used for modeling the plunging point and depth. A multi layer perceptron (MLP) is used as the ANN structure. A two-dimensional model is adapted to simulate density plunging flow through a reservoir with a sloping bottom. In the model, nonlinear and unsteady continuity, momentum, energy, and k-epsilon turbulence equations are formulated in the Cartesian coordinates. Density flow parameters such as velocity, plunging points, and plunging depths are determined from the simulation and model results, and these are compared with previous experimental and model works. The results show that the ANN model forecasts are much closer to the experimental data than the MLR and mathematical model forecasts.
Açıklama
Anahtar Kelimeler
Artificial Neural Networks (ANN), Dam, Density flow, Mathematical model, Plunging depth
Kaynak
Clean-Soil Air Water
WoS Q Değeri
Q2
Scopus Q Değeri
Q3
Cilt
38
Sayı
3
