Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Conference Object Upscaling Surface Flow Equations Depending Upon Data Availability at Different Scales(Springer Verlag, 2003) Tayfur, GökmenSt. Venant equations, which are used to model sheet flows, are point-scale, depth-averaged equations, requiring data on model parameters at a very fine scale. When data are available at the scale of a hillslope transect, the point equations need to be upscaled to conserve the mass and momentum at that scale, Hillslope-scale upscaled model must be developed if data are available at that scale. The performance of the three models applied to simulate flows from non-rilled surfaces revealed that the hillslope-scale upscaled model performs as good as the point-scale model though it uses far less data. The transectionally-upscaled model slightly underestimates the observed data.Article Citation - WoS: 72Citation - Scopus: 79Artificial Neural Networks for Estimating Daily Total Suspended Sediment in Natural Streams(IWA Publishing, 2006) Tayfur, Gökmen; Güldal, VeyselEstimates of sediment loads in natural streams are required for a wide spectrum of water resources engineering problems from optimal reservoir design to water quality in lakes. Suspended sediment constitutes 75-95% of the total load. The nonlinear problem of suspended sediment estimation requires a nonlinear model. An artificial neural network (ANN) model has been developed to predict daily total suspended sediment (TSS) in rivers. The model is constructed as a three-layer feedforward network using the back-propagation algorithm as a training tool. The model predicts TSS rates using precipitation (P) data as input. For network training and testing 240 sets of data sets were used. The model successfully predicted daily TSS loads using the present and past 4 days precipitation data in the input vector with R2 = 0.91 and MAE = 34.22 mg/L. The performance of the model was also tested against the most recently developed non-linear black box model based upon two-dimensional unit sediment graph theory (2D-USGT). The comparison of results revealed that the ANN has a significantly better performance than the 2D-USGT. Investigation results revealed that the ANN model requires a period of more than 75 d of measured P-TSS data for training the model for satisfactory TSS estimation. The statistical parameter range (xmin - xmax) plays a major role for optimal partitioning of data into training and testing sets. Both sets should have comparable values for the range parameter.Article Citation - WoS: 2Citation - Scopus: 2Trait-based heterogeneous populations plus (TbHP+) genetic algorithm(Elsevier Ltd., 2009) Tayfur, Gökmen; Sevil, Hakkı Erhan; Gezgin, Erkin; Özdemir, SerhanThis study developed a variant of genetic algorithm (GA) model called the trait-based heterogeneous populations plus (TbHP+). The developed TbHP+ model employs a memory concept in the form of immunity and instinct to provide the populations with a more efficient guidance. Also, it has an ability to vary the number of individuals during the search process, thus allowing an automatic determination of the size of the population based on the individual qualities such as character fitness and credit for immunity. The algorithm was tested against the classical GA model in convergence and minimum error performance. For this purpose, 5 different mathematical functions from the literature were employed. The selected functions have different topological characteristics, ranging from simple convex curves with 2 variables to complex trigonometric ones having several hilly shapes with more than 2 variables. The developed model and the classical GA model were applied to finding the global minima of the functions. The comparison of the results revealed that the developed TbHP+ model outperformed the classical GA in faster convergence and minimum errors, which may be explained by the adaptive nature of the new paradigm.Article Citation - WoS: 24Citation - Scopus: 24GA-optimized model predicts dispersion coefficient in natural channels(IWA Publishing, 2009) Tayfur, GökmenModels whose parameters were optimized by genetic algorithm (GA) were developed to predict the longitudinal dispersion coefficient in natural channels. Following the existing equations in the literature, ten different linear and nonlinear models were first constructed. The models relate the dispersion coefficient to flow and channel characteristics. The GA model was then employed to find the optimal values of the constructed model parameters by minimizing the mean absolute error function (objective function). The GA model utilized an 80% cross-over rate and 4% mutation rate. It started each computation with a population of 100 chromosomes in the gene pool. For each model, while minimizing the objective function, the values of the model parameters were constrained between [-10, +10] at each iteration. The optimal values of the model parameters were obtained using a calibration set of 54 out of 80 sets of measured data. The minimum error was obtained for the case where the model was a linear equation relating dispersion coefficient to flow discharge. The model performance was then satisfactorily tested against the remaining 26 measured validation datasets. It performed better than the existing equations. it yielded minimum errors of MAE = 21.4m2/s (mean absolute error) and RMSE = 28.5m2/s (root mean-squares error) and a maximum accuracy rate of 81%. © IWA Publishing 2009.Article Citation - WoS: 15Citation - Scopus: 17Genetic Algorithm-Based Discharge Estimation at Sites Receiving Lateral Inflows(American Society of Civil Engineers (ASCE), 2009) Tayfur, Gökmen; Barbetta, Silvia; Moramarco, TommasoThe genetic algorithm (GA) technique is applied to obtain optimal parameter values of the standard rating curve model (RCM) for predicting, in real time, event-based flow discharge hydrographs at sites receiving significant lateral inflows. The standard RCM uses the information of discharge and effective cross-sectional flow area at an upstream station and effective cross-sectional flow area wave travel time later at a downstream station to predict the flow rate at this last site. The GA technique obtains the optimal parameter values of the model, here defined as the GA-RCM model, by minimizing the mean absolute error objective function. The GA-RCM model was tested to predict hydrographs at three different stations, located on the Upper Tiber River in central Italy. The wave travel times characterizing the three selected river branches are, on the average, 4, 8, and 12h. For each river reach, seven events were employed, four for the model parameters' calibration and three for model testing. The GA approach, employing 100 chromosomes in the initial gene pool, 75% crossover rate, 5% mutation rate, and 10,000 iterations, made the GA-RCM model successfully simulate the hydrographs observed at each downstream section closely capturing the trend, time to peak, and peak rates with, on the average, less than 5% error. The model performance was also tested against the standard RCM model, which uses, on the contrary to the GA-RCM model, different values for the model parameters and wave travel time for each event, thus, making the application of the standard RCM for real time discharge monitoring inhibited. The comparative results revealed that the RCM model improved its performance by using the GA technique in estimating parameters. The sensitivity analysis results revealed that at most two events would be sufficient for the GA-RCM model to obtain the optimal values of the model parameters. A lower peak hydrograph can also be employed in the calibration to predict a higher peak hydrograph. Similarly, a shorter travel time hydrograph can be used in GA to obtain optimal model parameters that can be used to simulate floods characterized by longer travel time. For its characteristics, the GA-RCM model is suitable for the monitoring of discharge in real time, at river sites where only water levels are observed.Article Citation - WoS: 37Citation - Scopus: 49Predicting Suspended Sediment Loads and Missing Data for Gediz River, Turkey(American Society of Civil Engineers (ASCE), 2009) Ülke, Aslı; Tayfur, Gökmen; Özkul, SevinçPrediction of suspended sediment load (SSL) is important for water resources quantity and quality studies. The SSL of a stream is generally determined by direct measurement of the suspended sediment concentration or by employing sediment rating curve method. Although direct measurement is the most reliable method, it is very expensive, time consuming, and, in many instances, problematic for inaccessible sections, especially during floods. On the other hand, measuring precipitation and flow discharge is relatively easier and hence, there are more rain and flow gauging stations than SSL gauging stations in Turkey. Furthermore, due to its cost, measurements of SSL are carried out in longer periods compared to precipitation and flow measurements. Although daily precipitation and flow measurements are available for most of the Turkish river basins, at best semimonthly measurements are available for SSL. As such, it is essential to predict SSL from precipitation and flow data and to fill the gap for the missing data records. This study employed artificial intelligence methods of artificial neural networks (ANN) and neurofuzzy inference system, the sediment rating curve method, multilinear regression, and multinonlinear regression methods for this purpose. The comparative analysis of the results showed that the artificial intelligence methods have superiority over the other methods for predicting semimonthly suspended sediment loads. The ANN using conjugate gradient optimization method showed the best performance among the proposed models. It also satisfactorily generated daily SSL data for the missing period record of Gediz River, Turkey.Article Citation - WoS: 26Citation - Scopus: 31Groundwater Quality and Hydrogeochemical Properties of Torbali Region, Izmir, Turkey(Springer Verlag, 2008) Tayfur, Gökmen; Kırer, Tuğba; Baba, AlperThe large demand for drinking, irrigation and industrial water in the region of Torbalö (Izmir, Turkey) is supplied from groundwater sources. Almost every factory and farm has private wells that are drilled without permission. These cause the depletion of groundwater and limiting the usage of groundwater. This study investigates spatial and temporal change in groundwater quality, relationships between quality parameters, and sources of contamination in Torbali region. For this purpose, samples were collected from 10 different sampling points chosen according to their geological and hydrogeological properties and location relative to factories, between October 2001 and July 2002. Various physical (pH, temperature, EC), chemical (calcium, magnesium, potassium, sodium, chloride, alkalinity, copper, chromium, cadmium, lead, zinc) and organic (nitrate, nitrite, ammonia, COD and cyanide) parameters were monitored. It was observed that the groundwater has bicarbonate alkalinity. Agricultural contamination was determined in the region, especially during the summer. Nitrite and ammonia concentrations were found to be above drinking water standard. Organic matter contamination was also investigated in the study area. COD concentrations were higher than the permissible limits during the summer months of the monitoring period.Article Citation - WoS: 21Citation - Scopus: 25Predicting Hourly-Based Flow Discharge Hydrographs From Level Data Using Genetic Algorithms(Elsevier Ltd., 2008) Tayfur, Gökmen; Moramarco, TommasoThis study developed a genetic algorithm model to predict flow rates at sites receiving significant lateral inflow. It predicts flow rate at a downstream station from flow stage measured at upstream and downstream stations. For this purpose, it constructed two different models: First is analogous to the rating curve model (RCM) of Moramarco et al. [Moramarco, M., Barbetta, S., Melone, F., Singh, V.P., 2005. Relating local stage and remote discharge with significant lateral inflow. J. Hydrologic Eng., ASCE, 10(1)] and the second is based on summation of contributions from upstream station and lateral inflows using kinematic wave approximation. The model was applied to predict flow rates at three different gauging stations located on Tiber River, Upper Tiber River Basin, Italy. The model used average wave travel time for each river reach and obtained average set of parameter values for all the events observed in the same river reach. The GA model was calibrated, for each river reach and for each formulation, by three events and tested against three other events. The results showed that the GA model produced satisfactory results and it was superior over the most recently developed rating curve method. This study further analyzed the case where only water surface elevation data were used in the input vector to predict flow rates. The results showed that using elevation data produces satisfactory results. This has an implication for predicting flow rates at ungauged river sites since the surface elevation data can be obtained without needing the detailed geometry of river section which could change significantly during a flood.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.
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