Modelling of air temperature using remote sensing and artificial neural network in Turkey
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Tarih
2012
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The aim of this research was to forecast monthly mean air temperature based on remote sensing and artificial neural network (ANN) data by using twenty cities over Turkey. ANN contained an input layer, hidden layer and an output layer. While city, month, altitude, latitude, longitude, monthly mean land surface temperatures were chosen as inputs, and monthly mean air temperature was chosen as output for network. Levenberg-Marquardt (LM) learning algorithms and tansig, logsig and linear transfer functions were used in the network. The data of Turkish State Meteorological Service (TSMS) and Technological Research Council of Turkey-Bilten for the period from 1995 to 2004 were chosen as training when the data of 2005 year were being used as test. Result of research was evaluated according to statistical rules. The best linear correlation coefficient (R), and root mean squared error (RMSE) between the estimated and measured values for monthly mean air temperature with ANN and remote sensing method were found to be 0.991-1.254 K, respectively. (C) 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Air temperature, Artificial neural network, NOAA, AVHRR, Remote sensing, Satellite
Kaynak
Advances in Space Research
WoS Q Değeri
Q3
Scopus Q Değeri
Q1
Cilt
50
Sayı
7
