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https://hdl.handle.net/11499/6837
Title: | Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey | Authors: | Altan Dombaycı, Ömer Gölcü, Mustafa |
Keywords: | Ambient temperature Artificial neural network Prediction Backpropagation Backpropagation algorithms Forecasting Image classification MATLAB Military operations Neural networks Neurons Temperature Ambient temperatures Ann models Artificial neural network models Artificial neural networks Case studies Computer programs Hidden layer neurons Hidden layers Hidden neurons Levenberg-marquardt Optimal network architectures Reliable models Squared errors Temperature values Testing datums Training algorithms Training datums Turkishs Network architecture air temperature algorithm artificial neural network software Denizli [Turkey] Eurasia Turkey |
Abstract: | The objective of this paper is to develop an artificial neural network (ANN) model which can be used to predict daily mean ambient temperatures in Denizli, south-western Turkey. In order to train the model, temperature values, measured by The Turkish State Meteorological Service over three years (2003-2005) were used as training data and the values of 2006 were used as testing data. In order to determine the optimal network architecture, various network architectures were designed; different training algorithms were used; the number of neuron and hidden layer and transfer functions in the hidden layer/output layer were changed. The predictions were performed by taking different number of hidden layer neurons between 3 and 30. The best result was obtained when the number of the neurons is 6. The selected ANN model of a multi-layer consists of 3 inputs, 6 hidden neurons and 1 output. Training of the network was performed by using Levenberg-Marquardt (LM) feed-forward backpropagation algorithms. A computer program was performed under Matlab 6.5 software. For each network, fraction of variance (R2) and root-mean squared error (RMSE) values were calculated and compared. The results show that the ANN approach is a reliable model for ambient temperature prediction. © 2008 Elsevier Ltd. All rights reserved. | URI: | https://hdl.handle.net/11499/6837 https://doi.org/10.1016/j.renene.2008.07.007 |
ISSN: | 0960-1481 |
Appears in Collections: | Denizli Teknik Bilimler Meslek Yüksekokulu Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection Teknik Eğitim Fakültesi Koleksiyonu WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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