Volume 12 [2010]

An artificial neural network (ANN) model-based approach was developed and applied to estimate
values of air temperature and relative humidity in remote mountainous areas. The application site
was the mountainous area of the Samaria National Forest canyon (Greece). Seven meteorological
stations were established in the area and ANNs were developed to predict air temperature and
relative humidity for the five most remote stations of the area using data only from two stations
located in the two more easily accessed sites. Measured and model-estimated data were compared
in terms of the determination coefficient (R2), the mean absolute error (MAE) and residuals
normality. Results showed that R2 values range from 0.7 to 0.9 for air temperature and from 0.7 to
0.8 for relative humidity whereas MAE values range from 0.9 to 1.8 oC and 5 to 9%, for air
temperature and relative humidity, respectively. In conclusion, the study demonstrated that ANNs,
when adequately trained, could have a high applicability in estimating meteorological data values in
remote mountainous areas with sparse network of meteorological stations, based on a series of
relatively limited number of data values from nearby and easily accessed meteorological stations.

Human thermal comfort conditions were determined in two mountainous regions of Greece, Gerania
mountains (MG) in east continental Greece, and mountainous Nafpaktia (MN) in west continental
Greece. Both regions are unexploited with considerable tourist potential. Four sites in each study
region were selected on the basis of different altitude. Air temperature and humidity, 1.5 m above
ground surface, were recorded simultaneously every 15 minutes by sensors with dataloggers in
selected sites between 23 June and 28 August 2007. Data of the above parameters were used for
the calculation of the thermohygrometric index from which thermal comfort conditions were
evaluated. Also, an artificial neural network model, was applied for the THI values evaluation at the
highest examined altitudes based on the respective values of the lowest examined altitudes in both
MG and MN. Results showed that from 09:00 to 20:00 h, MN was found to be more suitable, in
relation to MG, for tourist and recreation activities at altitudes of 1338 m. At lower altitudes, both
study regions could be proposed in an equal basis for the above activities during summer. Also, for
the same period, thermal comfort conditions at the highest examined altitudes of MN and MG can
accurately be predicted using artificial neural network models on the basis of those at lowest
examined altitudes. From 21:00 to 08:00 h, MN can be considered clearly as a better tourist
destination than MG.