WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - WoS: 5Citation - Scopus: 5Developing Predictive Equations for Water Capturing Performance and Sediment Release Efficiency for Coanda Intakes Using Artificial Intelligence Methods(MDPI, 2022) Hazar, Oğuz; Tayfur, Gökmen; Elçi, Şebnem; Singh, Vijay P.Estimation of withdrawal water and filtered sediment amounts are important to obtain maximum efficiency from an intake structure. The purpose of this study is to develop empirical equations to predict Water Capturing Performance (WCP) and Sediment Release Efficiency (SRE) for Coanda type intakes. These equations were developed using 216 sets of experimental data. Intakes were tested under six different slopes, six screens, and three water discharges. In SRE experiments, sediment concentration was kept constant. Dimensionless parameters were first developed and then subjected to multicollinearity analysis. Then, nonlinear equations were proposed whose exponents and coefficients were obtained using the Genetic Algorithm method. The equations were calibrated and validated with 70 and 30% of the data, respectively. The validation results revealed that the empirical equations produced low MAE and RMSE and high R2 values for both the WCP and the SRE. Results showed outperformance of the empirical equations against those of MNLR. Sensitivity analysis carried out by the ANNs revealed that the geometric parameters of the intake were comparably more sensitive than the flow characteristics.Article Citation - WoS: 54Citation - Scopus: 65Flood Hydrograph Prediction Using Machine Learning Methods(MDPI Multidisciplinary Digital Publishing Institute, 2018) Tayfur, Gökmen; Singh, Vijay P.; Moramarco, Tommaso; Barbetta, SilviaMachine learning (soft) methods have a wide range of applications in many disciplines, including hydrology. The first application of these methods in hydrology started in the 1990s and have since been extensively employed. Flood hydrograph prediction is important in hydrology and is generally done using linear or nonlinear Muskingum (NLM) methods or the numerical solutions of St. Venant (SV) flow equations or their simplified forms. However, soft computing methods are also utilized. This study discusses the application of the artificial neural network (ANN), the genetic algorithm (GA), the ant colony optimization (ACO), and the particle swarm optimization (PSO) methods for flood hydrograph predictions. Flow field data recorded on an equipped reach of Tiber River, central Italy, are used for training the ANN and to find the optimal values of the parameters of the rating curve method (RCM) by the GA, ACO, and PSO methods. Real hydrographs are satisfactorily predicted by the methods with an error in peak discharge and time to peak not exceeding, on average, 4% and 1%, respectively. In addition, the parameters of the Nonlinear Muskingum Model (NMM) are optimized by the same methods for flood routing in an artificial channel. Flood hydrographs generated by the NMM are compared against those obtained by the numerical solutions of the St. Venant equations. Results reveal that the machine learning models (ANN, GA, ACO, and PSO) are powerful tools and can be gainfully employed for flood hydrograph prediction. They use less and easily measurable data and have no significant parameter estimation problem.Article Citation - WoS: 10Citation - Scopus: 11Transport Capacity Models for Unsteady and Non-Equilibrium Sediment Transport in Alluvial Channels(Elsevier Ltd., 2012) Tayfur, Gökmen; Singh, Vijay P.This study investigates transport capacity models based on different dominant variables-shear stress, stream power, unit stream power, flow discharge, flow velocity, and energy slope - in a model of unsteady and non-equilibrium sediment transport in alluvial channels. The model simulates fully coupled system of water flow, suspended sediment, and bed load sediment transport processes in two-layer system of water flow phase and movable bed. The model employs conservation of mass equation for the water in both the layers; suspended sediment in the water flow phase; sediment in the movable bed layer; and the momentum equation for the water flow in the flow phase. The system is closed by relating the sediment flux in the movable bed layer to the sediment concentration in the same layer by employing the kinematic wave theory. Using the sediment transport capacity expression with different dominant variables, a series of numerical experiments are carried out for unsteady and non-equilibrium sediment transport. The results seem theoretically reasonable for hypothetical cases. The model is calibrated and validated using different experimental data sets. The calibrated value for the transport capacity model's exponent (ki) is found to be 1.50, 1.65, 0.24, 0.56, 4.80, and 0.22 for shear stress, stream power, unit stream power, discharge, velocity, and slope approaches, respectively. The numerical investigation results show that transport capacity model based on any dominant variable can be employed for modelling unsteady and non-equilibrium sediment transport.Article Citation - WoS: 28Citation - Scopus: 30Principle Component Analysis in Conjuction With Data Driven Methods for Sediment Load Prediction(Springer Verlag, 2013) Tayfur, Gökmen; Karimi, Yashar; Singh, Vijay P.This study investigates sediment load prediction and generalization from laboratory scale to field scale using principle component analysis (PCA) in conjunction with data driven methods of artificial neural networks (ANNs) and genetic algorithms (GAs). Five main dimensionless parameters for total load are identified by using PCA. These parameters are used in the input vector of ANN for predicting total sediment loads. In addition, nonlinear equations are constructed, based upon the same identified dimensionless parameters. The optimal values of exponents and constants of the equations are obtained by the GA method. The performance of the so-developed ANN and GA based methods is evaluated using laboratory and field data. Results show that the expert methods (ANN and GA), calibrated with laboratory data, are capable of predicting total sediment load in field, thus showing their transferability. In addition, this study shows that the expert methods are not transferable for suspended load, perhaps due to insufficient laboratory data. Yet, these methods are able to predict suspended load in field, when trained with respective field data.Article Citation - WoS: 22Citation - Scopus: 29Predicting Mean and Bankfull Discharge From Channel Cross-Sectional Area by Expert and Regression Methods(Springer Verlag, 2011) Tayfur, Gökmen; Singh, Vijay P.This study employed four methods-non-linear regression, fuzzy logic (FL), artificial neural networks (ANNs), and genetic algorithm (GA)-based nonlinear equation-for predicting mean discharge and bank-full discharge from cross-sectional area. The data compiled from the literature were separated into two groups-training (calibration) and testing (verification). Using training data sets, the methods were calibrated to obtain optimal values of the coefficients of the non-linear regression method; optimal number of fuzzy subsets, their base widths and fuzzy rules for the fuzzy method; and the optimal number of neurons in the hidden layer, the learning rate and momentum factor values for the ANN model. The GA-based method employed 100 chromosomes in the initial gene pool, 80% cross over rate and 4% mutation rate in determining the optimal values of the coefficients of the constructed nonlinear equation. The calibrated methods were then applied to the test data sets. The test results showed that the non-linear regression, ANN and GA-based methods were comparable in predicting the mean discharge while the fuzzy method produced high errors and low accuracy. The GA-based method had the highest accuracy of 75%. In terms of predicting bankfull discharge, all methods produced satisfactory results, although the fuzzy method had the lowest accuracy of 33%. The results of sensitivity analysis, which is limited to the GA-based and nonlinear regression methods, showed that the GA-based method calibrated with low bankfull discharge values can be successfully applied to predict high bankfull discharge values. This has important implications for predicting bankfull rates at ungauged sites. On the other hand, the sensitivity analysis results also showed that both the non-linear regression and GA-based methods have poor extrapolation capability for predicting mean discharge data.Article Citation - WoS: 3Citation - Scopus: 3Simulating Transient Sediment Waves in Aggraded Alluvial Channels by Double-Decomposition Method(American Society of Civil Engineers (ASCE), 2011) Tayfur, Gökmen; Singh, Vijay P.By using the double-decomposition (DD) method, this study simulates transient sediment waves caused by aggradation described by a diffusion-type partial differential equation (PDE). The DD method solves the PDE by decomposing the solution function for sediment rate into a summation of M number of components, where M stands for the order of approximation. The solution was approximated by considering only the first three terms. The model satisfactorily simulated laboratory-measured aggradation bed profiles with, on average, a mean absolute error (MAE) of 0.70 cm, a root-mean-square error (RMSE) of 0.84 cm, a mean relative error (MRE) of 1.11%, and R2=0.95. The model performance was also tested by using numerical and error-function solutions. In addition, the results obtained from application of the DD solution to hypothetical field cases were found to be theoretically compatible with what may be observed in natural streams. However, sediment wave fronts in later periods of the simulation time reached equilibrium bed levels more quickly, around in the middle section of the channel.Article Citation - WoS: 5Citation - Scopus: 5Kinematic Wave Theory for Transient Bed Sediment Waves in Alluvial Rivers(American Society of Civil Engineers (ASCE), 2008) Singh, Vijay P.; Tayfur, GökmenTransient bed sediment waves in alluvial rivers have been described using a multitude of hydraulic formulations. These formulations are based on some form of the St. Venant equations and conservation of mass of sediment in suspension and in bed. Depending on the assumptions employed, a hierarchy of formulations is expressed. These formulations in the literature employ uncoupled, semicoupled, or fully coupled transport models treating the sediment waves as either hyperbolic (dynamic wave) or parabolic (diffusion wave). It is, however, hypothesized that the movement of bed sediment waves in alluvial rivers can be described as a kinematic wave. Kinematic wave theory employs a functional relation between sediment transport rate and concentration and a relation between flow velocity and depth. This study summarizes the hierarchy of the formulations while emphasizing the kinematic wave theory for describing transient bed sediment waves. The applicability of the theory is shown for laboratory flume data and hypothetical cases.Annotation Citation - WoS: 1Citation - Scopus: 1Closure To "ann and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff" by Gokmen Tayfur and Vijay P. Singh(American Society of Civil Engineers (ASCE), 2008) Tayfur, Gökmen; Singh, Vijay P.We would like to thank Dr. Wong for his interest in and thoughts on our analysis of runoff hydrograph prediction and the goodnessof-fit measurement. We agree that visual comparison of simulated and measured hydrographs is an important indicator for assessing the performance of models. Visual inspection allows one to see intricate differences between hydrographs.Article Citation - WoS: 16Citation - Scopus: 18Kinematic Wave Model of Bed Profiles in Alluvial Channels(John Wiley and Sons Inc., 2006) Tayfur, Gökmen; Singh, Vijay P.A mathematical model, based on the kinematic wave (KW) theory, is developed for describing the evolution and movement of bed profiles in alluvial channels. The model employs a functional relation between sediment transport rate and concentration, a relation between flow velocity and depth and Velikanov's formula relating suspended sediment concentration to flow variables. Laboratory flume and field data are used to test the model. Transient bed profiles in alluvial channels are also simulated for several hypothetical cases involving different water flow and sediment concentration characteristics. The model-simulated bed profiles are found to be in good agreement with what is observed in the laboratory, and they seem theoretically reasonable for hypothetical cases. The model results reveal that the mean particle velocity and maximum concentration (maximum bed form elevation) strongly affect transient bed profiles.Article Citation - WoS: 103Citation - Scopus: 126Ann and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff(American Society of Civil Engineers (ASCE), 2006) Tayfur, Gökmen; Singh, Vijay P.This study presents the development of artificial neural network (ANN) and fuzzy logic (FL) models for predicting event-based rainfall runoff and tests these models against the kinematic wave approximation (KWA). A three-layer feed-forward ANN was developed using the sigmoid function and the backpropagation algorithm. The FL model was developed employing the triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the measured data. The measured event based rainfall-runoff peak discharge data from laboratory flume and experimental plots were satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all the three models satisfactorily simulated event-based rainfall-runoff hydrographs from experimental plots with comparable error measures. ANN and FL models also satisfactorily simulated a measured hydrograph from a small watershed 8.44 km2 in area. The results provide insights into the adequacy of ANN and FL methods as well as their competitiveness against the KWA for simulating event-based rainfall-runoff processes.