Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Ampirik Yöntemlerle Gediz Nehri için Askıda Katı Madde Yükü Tahmini
    (Turkish Chamber of Civil Engineers, 2011) Ülke, Aslı; Özkul, Sevinç; Tayfur, Gökmen
    It is essential to predict suspended sediment load for understanding river morphology, design of dams, water supply problems, management of reservoirs and determination of pollution levels in rivers. The suspended sediment load can be determined by means of several methods such as direct measurements at the sediment gauging stations, sediment rating curve, son modeling methods, and empirical methods which are based on experimental works. The objective of this study is first to determine the best empirical method for Gediz river and then to improve the determined method by genetic algorithm (GA). It is seen that the GA improved Brooks method can be used for Gediz River Basin. In addition, this method was compared with other soft computing (ANN, ANFIS) methods and its performance is found to be as good as them.
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 6
    Genetic Algorithm-Artificial Neural Network Model for the Prediction of Germanium Recovery From Zinc Plant Residues
    (Taylor and Francis Ltd., 2002) Akkurt, Sedat; Özdemir, Serhan; Tayfur, Gökmen
    A multi-layer, feed-forward, back-propagation learning algorithm was used as an artificial neural network (ANN) tool to predict the extraction of germanium from zinc plant residues by sulphuric acid leaching. A genetic algorithm (GA) was used for the selection of training and testing data and a GA-ANN model of the germanium leaching system was created on the basis of the training data. Testing of the model yielded good error levels (r2 = 0.95). The model was employed to predict the response of the system to different values of the factors that affect the recovery of germanium and the results facilitate selection of the experimental conditions in which the optimum recovery will be achieved.