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

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. 1. Introduction Urban climate, in general, is characterized by higher air temperature ( ) values compared to those at adjacent rural and mountainous regions. Increased urban contributes to increased emissions of power plants pollutants and of smog production, higher energy consumption, particularly for air-conditioning [1], and unpleasant thermal conditions to people, mainly during the warmest period of the year [2, 3], resulting, from a climatic point of view, in the degradation of human life quality. These conditions are associated in a high degree with urbanization of cities which is characterized as one of the most powerful anthropogenic forces all over the world [4]. Mountainous regions are distinguishable from urban regions due to their special pattern based mainly on the impact of topography and composition of vegetation [5, 6]. It is well known that decreases as height increases in the troposphere [7], and a direct relationship has been reported between and the altitude of mountain regions [8]. These regions present improved bioclimatic conditions in relation to urban regions, especially in the summer, for example, the case of mountainous Nafpaktia [9], and they are used extensively as tourist destinations [10]. It has been reported that the microclimatic conditions in urban regions, for example, Athens, are influenced mainly by the ground cover type, which relates to the