Yazar "Dursun, Omer Faruk" seçeneğine göre listele

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    Ensemble and optimized hybrid algorithms through Runge Kutta optimizer for sewer sediment transport modeling using a data pre-processing approach
    (Irtces, 2023) Gul, Enes; Safari, Mir Jafar Sadegh; Dursun, Omer Faruk; Tayfur, Gokmen
    Uncontrolled sediment deposition in drainage and sewer systems raises unexpected maintenance expenditures. To this end, implementation of an accurate model relying on effective parameters involved is a reliable benchmark. In this study, three machine learning techniques, namely extreme learning machine (ELM), multilayer perceptron neural network (MLPNN), and M5P model tree (M5PMT); and three optimization approaches of Runge Kutta (RUN), genetic algorithm (GA), and particle swarm optimization (PSO) are applied for modeling. The optimization and ensemble hybridization approaches are applied in the modeling procedure. For the case of hybrid optimized models, the ELM and MLPNN models are hybridized with RUN, GA, and PSO algorithms to develop six hybrid models of ELM-RUN, ELM-GA, ELMPSO, MLPNN-RUN, MLPNN-GA, and MLPNN-PSO. Ensemble hybrid models are developed through coupling the ELM and MLPNN models with the M5PMT algorithm. The data pre-processing approach is applied to find the best randomness characteristic of the utilized data. Results illustrate that the RUNbased hybrid models outperform the GA- and PSO-based counterparts. Although the MLPNN-RUN and MLPNN-M5PMT hybrid models generate better results than their alternatives, MLPNN-M5PMT slightly outperforms MLPNN-RUN model with a coefficient of determination of 0.84 and a root mean square error of 0.88. The current study shows the superiority of the ensemble-based approach to the optimization techniques. Further investigation is needed by considering alternative optimization techniques to enhance sediment transport modeling. (c) 2023 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research. Published by Elsevier B.V. All rights reserved.
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    Hydrological Properties of the Derme Karstic Springs by Using Hydrogeochemical Analyses and Environmental Isotope Techniques
    (Wiley-Blackwell, 2016) Dursun, Omer Faruk; Celiker, Murat; Firat, Mahmut
    In this study, determination of recharging mechanism of the Derme karst spring by hydrogeochemical analysis and environmental isotope techniques are aimed. The Derme karst spring is an important karst spring that is located in the eastern part of Turkey, meets the domestic water need of Malatya province having a population of 750000, and has an average flow rate of 2.7m(3)/s. Water samples were taken from eight different springs from May to October 2011 for physicochemical and isotopic analyses (oxygen 18, deuterium, tritium) in order to determine the catchment mechanisms and the hydrodynamic structures of these karstic springs. The hydrogeochemical classification of spring waters was performed through a piper diagram. The results indicated that all of the springs were characterized by the calcium bicarbonate type. It was found that the deuterium excess value of the karstic springs was ranging from 13.42 to 18.84%, and that these springs were fed by both territorial and marine origin rainfalls. It was considered that the springs in the karstic region were under the influence of different karstic systems.
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    Prediction of aeration efficiency of parshall and modified venturi flumes: application of soft computing versus regression models
    (Iwa Publishing, 2021) Sihag, Parveen; Dursun, Omer Faruk; Sammen, Saad Shauket; Malik, Anurag; Chauhan, Anita
    In this study, the potential of soft computing techniques namely Random Forest (RF), M5P, Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH) was evaluated to predict the aeration efficiency (AE(20)) at Parshall and Modified Venturi flumes. Experiments were conducted for 26 various Modified Venturi flumes and one Parshall flume. A total of 99 observations were obtained from experiments. The results of soft computing models were compared with regression-based models (i.e., MLR: multiple linear regression, and MNLR: multiple nonlinear regression). Results of the analysis revealed that the MARS model outperformed other soft computing and regression-based models for predicting the AE(20) at Parshall and Modified Venturi flumes with Pearson's correlation coefficient (CC) = 0.9997, and 0.9992, and root mean square error (RMSE) = 0.0015, and 0.0045 during calibration and validation periods. Sensitivity analysis was also carried out by using the best executing MARS model to assess the effect of individual input variables on AE(20) of both flumes. Obtained results on sensitivity examination indicate that the oxygen deficit ratio (r) was the most effective input variable in predicting the AE(20) at Parshall and Modified Venturi flumes.
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    Soft computing-based model development for estimating the aeration efficiency through Parshall flume and Venturi flumes
    (Springernature, 2023) Puri, Diksha; Sihag, Parveen; Sadeghifar, Tayeb; Dursun, Omer Faruk; Thakur, Mohindra Singh
    This study compares the efficacy of soft computing techniques namely, Random Forest, M5P tree and Adaptive Neuro Fuzzy Inference System to predict the aeration efficiency through a combined dataset of Parshall and modified Venturi Flumes. For the development and validation of the model, in all, 99 experimental observations were used. The model's development and validation were done by utilizing six independent variables, discharge, throat width, throat length, sill height, oxygen deficit ratio and the exponent factor as inputs whereas aeration efficiency was considered as a target. The performance of developed models is measured using six different goodness of fit parameters which are correlation coefficient, coefficient of determination, mean absolute error, mean squared error, root mean square error and mean absolute percentage error. Outcomes of the present analysis revealed that all developed models are capable of handling prediction due to their higher correlation coefficient (CC) values. However, Random Forest model outperformed other soft computing-based models for estimating the aeration efficiency with a correlation coefficient of 0.9981, mean absolute error value of 0.0023, and a mean squared error being 0.00 in the testing stage. Further, results obtained from sensitivity investigation indicate that the oxygen deficit ratio which contains the elements of saturated oxygen concentration, upstream oxygen concentration, and downstream oxygen concentration is the most effective input variable for estimating the aeration efficiency using this data set. Since oxygen deficit is highly sensitive to aeration efficiency, the values of saturated oxygen concentration, upstream and downstream oxygen concentration require due consideration.
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    Trend analyses for discharge-recharge of Tacin karstic spring (Kayseri, Turkey)
    (Pergamon-Elsevier Science Ltd, 2021) Celiker, Murat; Yukseler, Ufuk; Dursun, Omer Faruk
    Vast research is conducted around the world in order to determine the effects of climate change on water resources. Determining and preserving the rechanging characteristics of karstic springs has an important place in water resources management studies. In the present study, the variation trends of climate parameters, affecting the discharge trend of the Tacin karstic spring in Kayseri, Turkey and the spring discharge, were analyzed. For this purpose, the monthly mean discharge flow of the karstic spring between the years 1975 and 2017 and the monthly average temperatures and precipitation parameters belonging to the meteorological stations close to the spring (Kayseri, Pinarbast, Gemerek and Sarlosla) were adopted. In the trend analyses, the non-parametric Spearman Rho (SR), Mann Kendall (MK) and sen-Innovative Trend analysis tests (Sen-ITA) were used in addition to the non-linear tests. Accordingly, a reduction was observed in the change trend of the precipitation parameter of Pinarbasi and Kayseri stations and an increase in the respective measure of the Gemerek and Sarlosla stations while a noticeable decrease took place in the flow discharge of the Tacin karstic spring. The temperature parameter underwent a similar increase in all of the four stations meanwhile. Consequently, the decrease trend in the flow discharges of the Tacin karstic aquifer was inferred to stem from the reduction in the precipitation trend, observed in the southern part of the karstic aquifer.