Please use this identifier to cite or link to this item: 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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