Table of Contents
ISRN Meteorology
Volume 2013, Article ID 489350, 7 pages
http://dx.doi.org/10.1155/2013/489350
Research Article

Air Temperature Estimation by Using Artificial Neural Network Models in the Greater Athens Area, Greece

1Laboratory of General and Agricultural Meteorology, Agricultural University of Athens, Iera Odos 75, 118 55 Athens, Greece
2Laboratory of Physics, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece

Received 8 July 2013; Accepted 12 September 2013

Academic Editors: F. Acs and T. Georgiadis

Copyright © 2013 A. P. Kamoutsis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Air temperature (T) data were estimated in the regions of Nea Smirni, Penteli, and Peristeri, in the greater Athens area, Greece, using the T data of a reference station in Penteli. Two artificial neural network approaches were developed. The first approach, MLP1, used the T as input parameter and the second, MLP2, used additionally the time of the corresponding T. One site in Nea Smirni, three sites in Penteli, from which two are located in the Pentelikon mountain, and one site in Peristeri were selected based on different land use and altitude. T data were monitored in each site for the period between December 1, 2009, and November 30, 2010. In this work the two extreme seasons (winter and summer) are presented. The results showed that the MLP2 model was better (higher and lower MAE) than MLP1 for the T estimation in both winter and summer, independently of the examined region. In general, MLP1 and MLP2 models provided more accurate T estimations in regions located in greater distance (Nea Smirni and Peristeri) from the reference station in relation to the nearby Pentelikon mountain. The greater distance T estimations, in most cases, were better in winter compared to summer.