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

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• 作者：Fatih üne? ; Darko Joksimovic ; Ozgur Kisi
• 关键词：Neuro ; fuzzy ; Plunging depth ; Dam reservoir ; Density flow ; Mathematical model ; Neural network
• 刊名：Water Resources Management
• 出版年：2015
• 出版时间：July 2015
• 年：2015
• 卷：29
• 期：9
• 页码：3055-3077
• 全文大小：1,632 KB
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• 作者单位：Fatih üne? (1)
  Darko Joksimovic (2)
  Ozgur Kisi (3)
  
  1. Engineering Faculty, Civil Engineering Department, Hydraulics Division, Mustafa Kemal University, 31040, Antakya, Hatay, Turkey
  2. Engineering Faculty, Civil Engineering Department, Hydraulics Division, Ryerson University, Toronto, Canada
  3. Faculty of Architecture and Engineering, Civil Engineering Department, Canik Basari University, Samsun, Turkey
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geotechnical Engineering
  Meteorology and Climatology
  Civil Engineering
  Environment
  
• 出版者：Springer Netherlands
• ISSN：1573-1650

文摘

The 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-ε 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 (R2) 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-.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.