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    Prediction of Flood Frequency Factor for Gumbel Distribution Using Regression and GEP Model
    (Springer Heidelberg, 2017) Onen, Fevzi; Bagatur, Tamer
    Floods are the most common natural disasters that affect societies around the world. One of the major problems in water resources engineering design is the estimation of maximum flood discharges. These estimations are determined to assign hydrological and hydraulic dimensions to bridges, sewers, dam, spillway, protection embankments, weirs, detention ponds and diversion canals. Accurate estimation of flood frequency discharge increases safety of the hydraulic structures. In probability theory and statistics, flood frequency analysis is used to obtain the probability distribution of floods. The distribution models can be summarized the generalized extreme value, Gumbel or extreme value type 1, Log-Normal and the Log Pearson type III distributions. The Gumbel distribution provides the best fit according to the extreme value analysis studies. This study concentrates on prediction of flood frequency factor (K) for the Gumbel distribution using gene expression programming (GEP) and regression model. Some prediction models are presented for determining of flood frequency factor (K). The proposed regression model (Model 4) and GEP model (Model 7) give a fast and practical way of estimating the flood frequency factor. Thus, Gumbel's method has been simplified in such a predictive model that one can obtain the magnitude of a given return period for flood discharges without recourse to looking at a table. The performance of the prediction models was evaluated with an illustrative example for 2, 5, 10, 20, 50, 100, 200, 250, 500 and 1000 years flood.
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    Prediction of Flow and Oxygen Transfer by a Plunging Water Jets with Genetic Expression Programming (GEP) Models
    (Springer Heidelberg, 2014) Bagatur, Tamer; Onen, Fevzi
    A plunging water jet passing through the surrounding air entrains a large amount of air bubbles into a pool and forms a large submerged two-phase (gas-liquid) contacting area. This process is called air entrainment or aeration by a plunging water jets. In this study, the flow characteristics such as volumetric air entrainment rate, bubble penetration depth and oxygen transfer efficiency are evaluated based on five major parameters which describe air entrainment at the plunge point: the nozzle diameter, jet length, jet velocity, nozzle length-to-diameter ratio and jet impact angle. This paper presents gene expression programming (GEP) model, which is an extension to genetic programming, as an alternative approach to modeling of the flow characteristics such as the bubble penetration depth, air entrainment rate and oxygen transfer efficiency by plunging water jets. New formulations for prediction of the flow characteristics in the plunging water jet system are developed using GEP and regression models.
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    Prediction of penetration depth in a plunging water jet using soft computing approaches
    (Springer London Ltd, 2014) Onen, Fevzi
    The flow characteristics of the plunging water jets can be defined as volumetric air entrainment rate, bubble penetration depth, and oxygen transfer efficiency. In this study, the bubble penetration depth is evaluated based on four major parameters that describe air entrainment at the plunge point: the nozzle diameter (D (N)), jet length (L (j)), jet velocity (V (N)), and jet impact angle (theta). This study presents artificial neural network (ANN) and genetic expression programming (GEP) model, which is an extension to genetic programming, as an alternative approach to modeling of the bubble penetration depth by plunging water jets. A new formulation for prediction of penetration depth in a plunging water jets is developed using GEP. The GEP-based formulation and ANN approach are compared with experimental results, multiple linear/nonlinear regressions, and other equations. The results have shown that the both ANN and GEP are found to be able to learn the relation between the bubble penetration depth and basic water jet properties. Additionally, sensitivity analysis is performed for ANN, and it is found that D (N) is the most effective parameter on the bubble penetration depth.
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    Prediction of Scour at a Side-Weir with GEP, ANN and Regression Models
    (Springer Heidelberg, 2014) Onen, Fevzi
    Side-weir is known as a lateral intake structure, which is widely used in irrigation, land drainage, and urban sewerage system by flow diversion device. Local scour in-/volves the removal of material around piers, abutments, side-weir, spurs, and embankments. Clear-water scour depth based on four dimensional parameters: approach flow velocity (V (1)/ V (c) ), water head ratio (h (1) - p)/h (1), side-weir length (L/b) and sediment size (d (50)/p). The equilibrium depth of scour increases by the increase of the dimensionless parameters of approach flow velocity, water head ratio, side-weir length and sediment size. This study presents artificial neural network (ANN) and gene expression programming (GEP) models, which is an algorithm based on genetic algorithms and genetic programming, for prediction of the clear-water scour depth at side-weir. The explicit formulations of the GEP models are developed. The GEP-based formulation and ANN approach are compared with experimental results, multiple linear/nonlinear regressions (MLR/MNLR). The performance of GEP is found in slightly more influential than ANN approach and MNLR for predicting the clear-water scour depth at side-weir.
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    A predictive model on air entrainment by plunging water jets using GEP and ANN
    (Korean Society Of Civil Engineers-Ksce, 2014) Bagatur, Tamer; Onen, Fevzi
    Plunging water jet flow situations are frequently encountered in nature and environmental engineering. A plunging liquid jet has the ability to provide vigorous gas-liquid mixing and dispersion of small bubbles in the liquid, and enhances mass transfer rate by producing larger gas-liquid interfacial area. This process is called air-entrainment or aeration by a plunging water jet. Advances in field of Artificial Intelligence (AI) offer opportunities of utilizing new algorithms and models. This study presents Artificial Neural Network (ANN) and Gene-Expression Programming (GEP) model, which is an extension to genetic programming, as an alternative approach to modeling of volumetric air entrainment rate by plunging water jets. A new formulation for prediction of volumetric air entrainment rate by plunging water jets using GEP is developed. The GEP-based formulation and ANN approach are compared with experimental results, Multiple Linear/Nonlinear Regressions (MLR/NMLR) and other equations. The results have shown that the both ANN and GEP are found to be able to learn the relation between volumetric air entrainment rate and basic water jet properties. Additionally, sensitivity analysis is performed and it is found that nozzle diameter is the most effective parameter on the volumetric air entrainment rate among water jet velocity, jet length and jet impact angle.
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    Testing of System Performance for Different Aerator Configuration Using Venturi
    (2018) Bagatur, Tamer; Kayaalp, Necati; Onen, Fevzi
    Abstract: A venturi tube or pipe part or device allows air bubbles to be inserted into flowing water from air inletholes and so increases dissolved oxygen (DO) levels in water. Therefore, the aim of this paper is to evaluatesystem design and experimental results related to configuration of venturi tube in air vacuum and aerationprocess. Different aerator modules constructed using venturi tubes connected in either single or double in parallel(with single or double outlet pipe line) were evaluated and compared for their air flowrate, vacuum capacity,oxygen transfer coefficients (OTC), standard oxygen transfer rate (SOTR), and standard oxygenation efficiency(SOE) determined by clean water tests. The experimental results indicated that the double parallel design(connected to a single outlet pipe line) generally performed better than the single and double parallel (connectedto a double outlet pipe line) design in terms of transferring oxygen into water.