Air Temperature Estimation by Using Artificial Neural Network Models in the Greater Athens Area, Greece. (28th October 2013)

• Record Type: Journal Article
• Authors: Kamoutsis, A. P.; Matsoukis, A. S.; Chronopoulos, K. I.
• Other Names: Acs F. Academic Editor.; Georgiadis T. Academic Editor.
• Is Part Of: ISRN meteorology. Volume 2013(2013)
• Publication Date: 2013-10-28
• Subjects: Meteorology -- Periodicals; Meteorology; Periodicals; Electronic journals; 551.5
• DOI: 10.1155/2013/489350
• Languages: English
• ISSNs: 2090-7516

Abstract

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 R 2 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.