Browsing by Author "Turan M.E."
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Item River flow estimation from upstream flow records by artificial intelligence methods(2009) Turan M.E.; Yurdusev M.A.Water resources management has become more and more crucial by the depletion of available water resources to use as opposed to the increase of the water consumption. An effective management relies on accurate and complete information about the river on which a project will be constructed. Artificial intelligence techniques are often and successfully used to complete the unmeasured data. In this study, feed forward back propagation neural networks, generalized regression neural network, fuzzy logic are used to estimate unmeasured data using the data of the four runoff gauge station on the Birs River in Switzerland. The performances of these models are measured by the mean square error, determination coefficients and efficiency coefficients to choose the best fit model. © 2009 Elsevier B.V. All rights reserved.Item Water use prediction by radial and feed-forward neural nets(2009) Yurdusev M.A.; Firat M.; Mermer M.; Turan M.E.In this study, applicability of feed-forward and radial-basis neural networks for monthly water consumption prediction from several socio-economic and climatic factors affecting water use is investigated. A data set including a total of 108 data records is divided into two subsets: training and testing. Firstly, the models based on a single input variable are trained and tested by feed-forward and radial methods and feed-forward and radial performances of the models are compared. Then, the models based on multiple input variables are constructed according to performances of the models based on a single input variable. The performances of feed-forward and radial models in training and testing phases are compared with the observations and the best-fit model is identified. For this purpose, several criteria such as normalised root mean square error, efficiency and correlation coefficient are calculated for all models. Subsequently, the best-fit models are also trained and tested by multiple linear regression for comparison. The results indicated that feed-forward and radial methods can be applied successfully for monthly water consumption prediction. © 2009 Thomas Telford.Item Comparative analysis of fuzzy inference systems for water consumption time series prediction(2009) Firat M.; Turan M.E.; Yurdusev M.A.Two types of fuzzy inference systems (FIS) are used for predicting municipal water consumption time series. The FISs used include an adaptive neuro-fuzzy inference system (ANFIS) and a Mamdani fuzzy inference systems (MFIS). The prediction models are constructed based on the combination of the antecedent values of water consumptions. The performance of ANFIS and MFIS models in training and testing phases are compared with the observations and the best fit model is identified according to the selected performance criteria. The results demonstrated that the ANFIS model is superior to MFIS models and can be successfully applied for prediction of water consumption time series. © 2009 Elsevier B.V. All rights reserved.Item Evaluation of artificial neural network techniques for municipal water consumption modeling(2009) Firat M.; Yurdusev M.A.; Turan M.E.Various Artificial Neural Network techniques such as Generalized Regression Neural Networks (GRNN), Feed Forward Neural Networks (FFNN) and Radial Basis Neural Networks (RBNN) have been evaluated based on their performance in forecasting monthly water consumptions from several socio-economic and climatic factors, which affect water use. The data set including total 108 data records is divided into two subsets, training and testing. The models consisting of the combination of the independent variables are constructed and the best fit input structure is investigated. The performance of ANN models in training and testing stages are compared with the observed water consumption values to identify the best fit forecasting model. For this purpose, some performance criteria such as Normalized Root Mean Square Error (NRMSE), efficiency (E) and correlation coefficient (CORR) are calculated for all models. The best fit models are also trained and tested by Multiple Linear Regression (MLR). The results indicated that GRNN outperforms all other methods in modeling monthly water consumptions. © Springer Science+Business Media B.V. 2008.Item Neural networks and fuzzy inference systems for predicting water consumption time series(2009) Yurdusev M.A.; Firat M.; Turan M.E.; Gultekin Sinir B.[No abstract available]Item Monthly river flow forecasting by an adaptive neuro-fuzzy inference system(2010) Firat M.; Turan M.E.In this study, the applicability of an adaptive neuro-fuzzy inference system (ANFIS) to forecast for monthly river flows is investigated. For this, the Göksu river in the Seyhan catchment located in southern Turkey was chosen as a case study. The river flow forecasting models having various input structures are trained and tested by the ANFIS method. The results of ANFIS models for both training and testing are evaluated and the best-fit forecasting model is determined. The best-fit model is also trained and tested by feed forward neural networks (FFNN) and traditional autoregressive (AR) methods, and the performances of the models are compared. Moreover, ANFIS and FFNN models are verified by a validation data set including river flow data records during the time period 1997-2000. The results demonstrate that ANFIS can be applied successfully and provides high accuracy and reliability for monthly river flow forecasting. © 2009 The Authors. Journal compilation.Item Erratum to Evaluation of artificial neural network techniques for municipal water consumption modeling (Water Resources Management, (2009), 23, (617-632), 10.1007/s11269-008-9291-3)(2010) Yurdusev M.A.; Firat M.; Turan M.E.[No abstract available]Item Comparative analysis of neural network techniques for predicting water consumption time series(2010) Firat M.; Turan M.E.; Yurdusev M.A.Monthly water consumption time series have been predicted using a series of Artificial Neural Network (ANN) techniques including Generalized Regression Neural Networks (GRNN), Cascade Correlation Neural Network (CCNN) and Feed Forward Neural Networks (FFNN). One hundred and eight data sets for the city of Izmir, Turkey are used for a number of ANN modeling exercises. Several ANN models depending on the combination of antecedent values of water consumption records are constructed and the best fit input structure is investigated. The performance of ANN models in training and testing stages are compared with the observed water consumption values to identify the best fit forecasting model based upon a number of selected performance criteria. © 2010 Elsevier B.V. All rights reserved.Item Reply to discussion on "River flow estimation from upstream flow records by artificial intelligence methods" by M.E. Turan and M.A. Yurdusev [J. Hydrol. 369 (2009) 71-77](2011) Yurdusev M.A.; Turan M.E.[No abstract available]Item Optimization of open canal cross sections by differential evolution algorithm(Association for Scientific Research, 2011) Turan M.E.; Yurdusev M.A.Open canals are important water transfer structures used in water resources systems. As such, they may require substantial amount of investment depending on its length and cross section. Therefore, cross section design should be carried out on an optimization basis. Traditionally, optimal sizing of open canal cross sections are undertaken by nonlinear optimization techniques such as Lagrange Multipliers. In this study, optimum cross sections of different canal geometries are obtained using differential evolution algorithm and the findings of these exercises are compared with those of given in related literature. It is observed that differential evolution algorithm can be well applicable to the problem and capable of giving the global optima. © Association for Scientific Research.Item Prediction of effects of microstructural phases using generalized regression neural network(2012) Ozturk A.U.; Turan M.E.In the scope of this study, microstructure-macroproperty relationship of cement mortars has been established in order to define the effects of microstructural phases on strength. Microstructural studies have been become great issue in materials engineering. Nowadays, to characterize the microstructural phase properties and to improve and modify them are performed by scientist to forecasting and enhancing. According to this objective, cement mortars incorporating with chemical admixtures were prepared to constitute different microstructural graphs. These micrographs were analyzed to determine the amounts of unhydrated cement part, undifferentiated hydrated part and capillary pore phases in the cement mortar sections. Afterwards, the amounts of these microstructural phases were related to strength values of each cement mortar specimen. The relationship was established by using generalized regression neural network analysis. © 2011 Elsevier Ltd. All rights reserved.Item Predicting Monthly River Flows by Genetic Fuzzy Systems(Kluwer Academic Publishers, 2014) Turan M.E.; Yurdusev M.A.Reliable flow forecasts are key to developing river regulation schemes such as reservoirs. River flow prediction has conventionally been undertaken by physical and black-box models. Several black-box type models have been employed to achieve this end. Of these, genetic fuzzy systems have been used in this study as they have relatively attracted limited attention to date. Genetic-fuzzy systems are the fuzzy systems that have the capability of learning and tuning by Genetic Algorithms. Employing two different fuzzy inference systems, a case study on Gediz river basin has been performed in an attempt to find a suitable genetic fuzzy system for flow prediction. © 2014, Springer Science+Business Media Dordrecht.Item Fuzzy Systems Tuned By Swarm Based Optimization Algorithms for Predicting Stream flow(Springer Netherlands, 2016) Turan M.E.River flow prediction is an important phenomenon in water resources for which different methods and perspective have been used. Using fuzzy system with black box perspective is one of them. Fuzzy systems have some parameters and properties that have to be determined. This is an optimization problem that can be solved by swarm optimization techniques among several techniques. Swarm optimization are developed by inspiring from the behavior of the animals living as swarm. The study presents two achievements fuzzy system that tuned by swarm optimization algorithms can be used for prediction of monthly mean streamflow and which swarm optimization algorithm is better than the others for tuning fuzzy systems. Three swarm optimization algorithms, hunter search, firefly, artificial bee colony are used in this study. These algorithms are compared with mean performance values and convergence speed. Monthly streamflow data of three stream gauging stations in Susurluk Basin are used for the case study. The results show, swarm optimization algorithms can be used for prediction of monthly mean streamflow and ABC algorithm has better performance values than other optimization algorithms. © 2016, Springer Science+Business Media Dordrecht.Item Fuzzy conceptual hydrological model for water flow prediction(Springer Science and Business Media B.V., 2016) Turan M.E.; Yurdusev M.A.Reliability in flow prediction is key to designing water resources projects. Over prediction may result in overdesign whereas under prediction brings about insufficient capacity solutions. While the former means insufficient use of financial resources, the latter may result in some water demand unmet. Therefore, so many techniques have been developed and used to make better flow prediction. In this study, this traditional problem is revisited in an attempt to improve the modeling performance of long used conceptual hydrological models. This is attained by incorporating fuzzy systems into a presently used conceptual model. The fuzzy integration process is carried out through the replacement of the storage elements of conceptual model by fuzzy systems. The case study undertaken has proved that the fuzzy conceptual model developed is quite competitive with ordinary conceptual model and promises improved predictions. © Springer Science+Business Media Dordrecht 2015.Item Feasible Sanitary Sewer Network Generation Using Graph Theory(Hindawi Limited, 2019) Turan M.E.; Bacak-Turan G.; Cetin T.; Aslan E.A graph theory-based methodology is proposed for the sewer system optimization problem in this study. Sewer system optimization includes two subproblems: layout optimization and hydraulic design optimization, which can be solved independently or solved simultaneously. No matter which method is chosen for the solution of the optimization problem, a feasible layout that satisfies the restrictions of the sewer system must be obtained in any step of the solution. There are two different layout options encountered: the layouts containing all sewer links and the layouts not containing all sewer links. The method proposed in this study generates a feasible sewer layout that contains all sewer links and satisfies all restrictions of a sanitary sewer system by using graph theory without any additional strategies unlike other studies. The method is applied to two different case studies. The results of the case studies have shown that graph theory is well applicable to sewer system optimization and the methodology proposed based on it is capable of generating a feasible layout. This study is expected to stimulate the use of graph theory on similar studies. © 2019 Mustafa Erkan Turan et al.Item Vulnerability of sewer network – graph theoretic approach(Desalination Publications, 2020) Ganesan B.; Raman S.; Ramalingam S.; Turan M.E.; Bacak-Turan G.One of the most important structures in urban areas is an efficient sewer system to protect humans and the environment from the detrimental effects of wastewater. Such sewer systems often consist of pipes, manholes, pumping stations, and other complementary units. Strict monitoring of the sewer system is highly essential as any leakage can cause undesirable effects on health and safety. The layout is modeled as a graph which contains all sewer links and satisfies all the restrictions of a sanitary sewer system. In this work, we apply centrality measures on the sewer network system and water distribution system and also analyze the vulnerability of these systems. © 2020 Desalination Publications. All rights reserved.Item Prediction of natural frequencies of Rayleigh pipe by hybrid meta-heuristic artificial neural network(Springer Science and Business Media Deutschland GmbH, 2023) Dagli B.Y.; Ergut A.; Turan M.E.This paper focuses on determination of the natural frequencies in slenderness pipe flows by considering fluid–structure interaction approach. Rayleigh beam theory is used to model the pipe. The fluid in the pipe is assumed as ideal, steady and uniform. Hamilton’s variation principle is demonstrated to obtain the equation of motion of pipe–fluid system. The dimensionless partial differential equations of motion are converted into matrix equations, and the values of natural frequencies of first three modes are archived with the analytical method. The results are arranged to be a data set for hybrid meta-heuristic artificial neural network (ANN) method. Three different meta-heuristic algorithms are used to train the ANN: particle swarm optimization (PSO) and artificial bee colony (ABC) and grey wolf optimizer (GWO). The comparison is presented to find a suitable algorithm based on accuracy for determining the natural frequency of the Rayleigh pipe conveying fluid. The results show that the PSO algorithm outperforms the other meta-heuristics in terms of performance indicators in prediction analysis. However, all algorithms and models can predict the natural frequencies with rate with satisfactory accuracy. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering.Item A New ANN Based Rapid Assessment Method for RC Residential Buildings(Taylor and Francis Ltd., 2023) Özkan E.; Demir A.; Turan M.E.This study is about the development of an Artificial Neural Network (ANN) based practical rapid assessment method for Reinforced Concrete (RC) buildings by using the minimum possible number of input data. The problem is formulated as a classification problem and evaluated as two sub-problems. Feed Forward Back Propagation (FFBP) and Generalized Regression Neural Networks (GRNNs) are used in each case and eight different ANN models are developed. To develop ANN models, a total of 402 residential building models are generated of three types and up to eight storeys. The earthquake performance of these building models is investigated through the nonlinear incremental mode combination method. By using the building properties as inputs and the results of structural analyses as outputs, the ANN models are trained and tested. Additionally, existing buildings are used for validation. The results show that the earthquake behavior of RC buildings can be predicted successfully using an ANN. © 2023 International Association for Bridge and Structural Engineering (IABSE).Item Analyzing the Effect of Sewer Network Size on Optimization Algorithms’ Performance in Sewer System Optimization(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Turan M.E.; Cetin T.Sewer systems are a component of city infrastructure that requires large investment in construction and operation. Metaheuristic optimization methods have been used to solve sewer optimization problems. The aim of this study is to investigate the effects of network size on metaheuristic optimization algorithms. Cuckoo Search (CS) and four versions of Grey Wolf Optimization (GWO) were utilized for the hydraulic optimization of sewer networks. The purpose of using different algorithms is to investigate whether the results obtained differ depending on the algorithm. In addition, to eliminate the parameter effect, the relevant algorithms were run with different parameters, such as population size. These algorithms were performed on three different-sized networks, namely small-sized, medium-sized, and large-sized networks. Friedman and Wilcoxon tests were utilized to statistically analyze the results. The results were also evaluated in terms of the optimality gap criterion. According to the results based on the optimality gap, the performance of each algorithm decreases as the network size increases. © 2024 by the authors.