Master Degree / Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/11147/3008
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Master Thesis Studying Seepage in a Body of Earth-Fill Dam by (artifical Neural Networks) Anns(Izmir Institute of Technology, 2006) Ersayın, Deniz; Tayfur, Gökmen; Tayfur, GökmenDams are structures that are used especially for water storage , energy production, and irrigation. Dams are mainly divided into four parts on the basis of the type and materials of construction as gravity dams, buttress dams, arch dams, and embankment dams. There are two types of embankment dams: earthfill dams and rockfill dams. In this study, seepage through an earthfill dam's body is investigated using an artificial neural network model. Seepage is investigated since seepage both in the dam's body and under the foundation adversely affects dam's stability. This study specifically investigated seepage in dam.s body. The seepage in the dams body follows a phreatic line. In order to understand the degree of seepage, it is necessary to measure the level of phreatic line. This measurement is called as piezometric measurement. Piezometric data sets which are collected from Jeziorsko earthfill dam in Poland were used for training and testing the developed ANN model. Jeziorsko dam is a non-homogeneous earthfill dam built on the impervious foundation. Artificial Neural Networks are one of the artificial intelligence related technologies and have many properties. In this study the water levels on the upstream and downstream sides of the dam were input variables and the water levels in the piezometers were the target outputs in the artificial neural network model. In the line of the purpose of this research, the locus of the seepage path in an earthfill dam is estimated by artificial neural networks. MATLAB 6 neural network toolbox is used for this study.Master Thesis Artificial Neural Networks Model for Air Quality in the Region of Izmir(Izmir Institute of Technology, 2002) Birgili, Savaş; Tayfur, GökmenIn this study, a systematic approach to the development of the artificial neural networks based forecasting model is presented. S02, and dust values are predicted with different topologies, inputs and transfer functions. Temperature and wind speed values are used as input parameters for the models. The back-propagation learning algorithm is used to train the networks. R 2 (correlation coefficient), and daily average errors are employed to investigate the accuracy of the networks. MATLAB 6 neural network toolbox is used for this study. The study results indicate that the neural networks are able to make accurate predictions even with the limited number of parameters. Results also show that increasing the topology of the network and number of the inputs, increases the accuracy of the network. Best results for the S02 forecasting are obtained with the network with two hidden layers, hyperbolic tangent function as transfer function and three input variables (R2 was found as 0,94 and daily average error was found as 3,6 j..lg/m3).The most accurate results for the dust forecasting are also obtained with the network with two hidden layer, hyperbolic tangent function as transfer function and three input variables (R2 was found as 0,92 and daily average error was found as 3,64 j..lg/m3).S02 and dust predictions using their last seven days values as an input are also studied, and R2 is calculated as 0,94 and daily average error is calculated as 4,03 Jlg/m3 for S02 prediction and R2 is calculated as 0,93 and daily average error is calculated as 4,32 Jlg/m3 for dust prediction and these results show that the neural network can make accurate predictions.