Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 13Citation - Scopus: 14Soil Erosion Model Tested on Experimental Data of a Laboratory Flume With a Pre-Existing Rill(Elsevier Ltd., 2020) Aksoy, Hafzullah; Gedikli, Abdullah; Yılmaz, Murat; Eriş, Ebru; Ünal, N. Erdem; Yoon, Jaeyoung; Tayfur, GökmenPrediction of sediment discharge transported within flow is strongly needed in order to provide measures for a well-established erosion control and water quality management practice. Initiated by runoff generation and erosion processes sediment transport is influenced by microtopography over hillslopes of hydrological watersheds. Consideration of microtopography provides more accurate results. In this study, a process-based two-dimensional rainfall-runoff mathematical model is coupled with erosion and sediment transport component. Both the rainfall-runoff and sediment transport components make simulations in rills and over interrill areas of a bare hillslope. Models at such fine resolution are rarely verified due to the complexity of rills and interrill areas. The model was applied on a data set compiled from laboratory experiments. Erosion flume was filled with granular sand to replace a bare soil. A longitudinal rill and an interrill area were pre-formed over the soil in the flume before the simulated rainfall exerted on. The flume was given both longitudinal and lateral slopes. The simulated rainfall was changed between 45 mm/h and 105 mm/h and exerted on granular uniform fine and medium sand in the erosion flume with longitudinal and lateral slopes both changing from 5% to 20%. Calibration of the model shows that it is able to produce good results in terms of sedigraphs, which suggest also that the model might be considered an important step to verify and improve watershed scale erosion and sediment transport models.Article Citation - WoS: 31Citation - Scopus: 34Data Pre-Post Processing Methods in Ai-Based Modeling of Seepage Through Earthen Dams(Elsevier Ltd., 2019) Sharghi, Elnaz; Nourani, Vahid; Behfar, Nazanin; Tayfur, GökmenIn this paper, seepage of Sattarkhan earthen dam in northwest Iran was simulated using various artificial intelligence (AI) models (e.g., Feed forward neural network, Adaptive neural fuzzy inference system and Support vector regression) and linear ARIMA model based on different input combinations. Both jittering pre-processing and ensembling post-processing methods were also used in order to enhance the performance of the used AI-based data driven methods. For this purpose, various jittered datasets were produced by imposing noises (at different levels) to the original time series to enlarge the training data sample space. Further, three techniques of simple linear, weighted linear and nonlinear neural averaging were considered for pre-post processing purpose. The obtained results indicated that using both jittering and ensembling (especially neural ensemble) enhanced the modeling performance by almost 30% in the testing phase. (C) 2019 Elsevier Ltd. All rights reserved.Annotation Citation - WoS: 1Citation - Scopus: 1Reply To Comment on “evaluation of a Physically Based Quasi-Linear and a Conceptually Based Nonlinear Muskingum Methods” by Reza Barati(Elsevier Ltd., 2017) Perumal, Muthiah; Tayfur, Gökmen; Rao, C. Madhusudana; Gürarslan, GürhanThe writers thank the discusser for his interest in the study of Perumal et al. (2017) and welcome the opportunity to address the issues raised by the discusser. The discusser has mainly raised four issues on the comparative study carried out by Perumal et al. (2017) in evaluating the performances of the VPMM model and the NLM based models, which was initiated by Gill (1977, 1978). These four issues are addressed by these writers in the following pages: As a first issue, the discusser has raised a question about the appropriateness of using the VPMM model (Perumal and Price, 2013), which he considers as the much improved routing model of the Muskingum-Cunge family approach, and the original nonlinear Muskingum model of Gill (1978), which he, perhaps, considers as a initial version of the NLM models. These writers perceive that the discusser intends to convey that the performance evaluation study presented by Perumal et al. (2017) based on a latest improved model and a initial version of the NLM models is inappropriate. Before discussing straightaway on this issue, the writer would like to clarify on the misconception of the discusser in categorizing the VPMM method and the Muskingum-Cunge method under one family approach.Article Citation - WoS: 23Citation - Scopus: 29Evaluation of a Physically Based Quasi-Linear and a Conceptually Based Nonlinear Muskingum Methods(Elsevier Ltd., 2017) Perumal, Muthiah; Tayfur, Gökmen; Rao, C. Madhusudana; Gürarslan, GürhanTwo variants of the Muskingum flood routing method formulated for accounting nonlinearity of the channel routing process are investigated in this study. These variant methods are: (1) The three-parameter conceptual Nonlinear Muskingum (NLM) method advocated by Gillin 1978, and (2) The Variable Parameter McCarthy-Muskingum (VPMM) method recently proposed by Perumal and Price in 2013. The VPMM method does not require rigorous calibration and validation procedures as required in the case of NLM method due to established relationships of its parameters with flow and channel characteristics based on hydrodynamic principles. The parameters of the conceptual nonlinear storage equation used in the NLM method were calibrated using the Artificial Intelligence Application (AIA) techniques, such as the Genetic Algorithm (GA), the Differential Evolution (DE), the Particle Swarm Optimization (PSO) and the Harmony Search (HS). The calibration was carried out on a given set of hypothetical flood events obtained by routing a given inflow hydrograph in a set of 40 km length prismatic channel reaches using the Saint-Venant (SV) equations. The validation of the calibrated NLM method was investigated using a different set of hypothetical flood hydrographs obtained in the same set of channel reaches used for calibration studies. Both the sets of solutions obtained in the calibration and validation cases using the NLM method were compared with the corresponding solutions of the VPMM method based on some pertinent evaluation measures. The results of the study reveal that the physically based VPMM method is capable of accounting for nonlinear characteristics of flood wave movement better than the conceptually based NLM method which requires the use of tedious calibration and validation procedures.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: 35Citation - Scopus: 36Coupling Soil Moisture and Precipitation Observations for Predicting Hourly Runoff at Small Catchment Scale(Elsevier Ltd., 2014) Tayfur, Gökmen; Zucco, Graziano; Brocca, Luca; Moramarco, TommasoThe importance of soil moisture is recognized in rainfall-runoff processes. This study quantitatively investigates the use of soil moisture measured at 10, 20, and 40cm soil depths along with rainfall in predicting runoff. For this purpose, two small sub-catchments of Tiber River Basin, in Italy, were instrumented during periods of October 2002-March 2003 and January-April 2004. Colorso Basin is about 13km2 and Niccone basin 137km2. Rainfall plus soil moisture at 10, 20, and 40cm formed the input vector while the discharge was the target output in the model of generalized regression neural network (GRNN). The model for each basin was calibrated and tested using October 2002-March 2003 data. The calibrated and tested GRNN was then employed to predict runoff for each basin for the period of January-April 2004. The model performance was found to be satisfactory with determination coefficient, R2, equal to 0.87 and Nash-Sutcliffe efficiency, NS, equal to 0.86 in the validation phase for both catchments. The investigation of effects of soil moisture on runoff prediction revealed that the addition of soil moisture data, along with rainfall, tremendously improves the performance of the model. The sensitivity analysis indicated that the use of soil moisture data at different depths allows to preserve the memory of the system thus having a similar effect of employing the past values of rainfall, but with improved GRNN performance.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: 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: 23Citation - Scopus: 24Modelling Sediment Transport From Bare Rilled Hillslopes by Areally Averaged Transport Equations(Elsevier Ltd., 2007) Tayfur, GökmenTreating the dynamics of sediment transport as two-dimensional on interrill-areas and as one-dimensional in rill sections, areally averaged sheet sediment transport equations are developed. The two-dimensional sheet sediment transport equation is averaged over an individual interrill-area width and then along the interrill-area length to obtain local-scale areally averaged interrill-area sheet sediment transport equation (local-scale areal averaging). Similarly, the cross-sectionally-averaged rill sediment transport equation is averaged along an individual rill length to obtain local-scale areally averaged rill sediment transport equation (local-scale areal averaging). In order to minimize computational effort and economize on the number of model parameters, the local-scale areally averaged equations are then averaged over a whole hillslope section (large-scale areal averaging). These equations constitute the areally averaged model. The expectations of the terms containing more than one variable are obtained by the method of regular perturbation. In the large-scale areal averaging it is assumed that all the randomness in the state variable is due to the randomness in the parameters of the process. Comparison of the results obtained from the areally averaged model with those of the point-scale model indicates that the areally averaged model uses far less data and yet it performs as well as the point-scale model. The results of the developed model indicate that on a rilled-surface most of the sediment loads comes from rill sections. The developed model is successfully tested against experimental data obtained from a bare rilled hillslope. It predicted measured runoff and sediment rates with mean absolute errors of 11.07 l/min and 0.382 kg/s, respectively.Article Citation - WoS: 53Citation - Scopus: 63Artificial Neural Network (ann) Prediction of Compressive Strength of Vartm Processed Polymer Composites(Elsevier Ltd., 2005) Seyhan, Abdullah Tuğrul; Tayfur, Gökmen; Karakurt, Murat; Tanoğlu, MetinA three layer feed forward artificial neural network (ANN) model having three input neurons, one output neuron and two hidden neurons was developed to predict the ply-lay up compressive strength of VARTM processed E-glass/ polyester composites. The composites were manufactured using fabric preforms consolidated with 0, 3 and 6 wt.% of thermoplastic binder. The learning of ANN was accomplished by a backpropagation algorithm. A good agreement between the measured and the predicted values was obtained. Testing of the model was done within low average error levels of 3.28%. Furthermore, the predictions of ANN model were compared with those obtained from a multi-linear regression (MLR) model. It was found that ANN model has better predictions than MLR model for the experimental data. Also, the ANN model was subjected to a sensitivity analysis to obtain its response. As a result, the ANN model was found to have an ability to yield a desired level of ply-lay up compressive strength values for the composites processed with the addition of the thermoplastic binder.