Assessment of bioclimatic comfort using artificial neural network models a preliminary study in a remote mountainous area of southern Greece /
This work presents an artificial neural network (ANN) model-based approach to assess bioclimatic conditions in remote mountainous areas using a relatively limited number of microclimatic data from easily accessible meteorological stations. Seven meteorological stations were established in the mounta...
Elmentve itt :
| Szerzők: | |
|---|---|
| Dokumentumtípus: | Cikk |
| Megjelent: |
2011
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| Sorozat: | Acta climatologica
44-45 |
| Kulcsszavak: | Bioklimatológia - Görögország |
| Tárgyszavak: | |
| Online Access: | http://acta.bibl.u-szeged.hu/16936 |
| LEADER | 02252nab a2200241 i 4500 | ||
|---|---|---|---|
| 001 | acta16936 | ||
| 005 | 20220511082938.0 | ||
| 008 | 161015s2011 hu o 0|| eng d | ||
| 022 | |a 0563-0614 | ||
| 040 | |a SZTE Egyetemi Kiadványok Repozitórium |b hun | ||
| 041 | |a eng | ||
| 100 | 1 | |a Chronopoulos K. | |
| 245 | 1 | 0 | |a Assessment of bioclimatic comfort using artificial neural network models |h [elektronikus dokumentum] : |b a preliminary study in a remote mountainous area of southern Greece / |c Chronopoulos K. |
| 260 | |c 2011 | ||
| 300 | |a 65-71 | ||
| 490 | 0 | |a Acta climatologica |v 44-45 | |
| 520 | 3 | |a This work presents an artificial neural network (ANN) model-based approach to assess bioclimatic conditions in remote mountainous areas using a relatively limited number of microclimatic data from easily accessible meteorological stations. Seven meteorological stations were established in the mountainous area of Samaria Forest canyon (Greece). ANN models 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 more easily accessible sites. Measured and model-estimated data were compared in terms of the determination coefficient, the mean absolute error and residuals normality. Then, the developed ANN models were used to predict values of the thermohygrometric (THI) bioclimatic index on hourly basis for the five most remote stations using the modelpredicted air temperature and humidity data and to evaluate the comfort THI categories. These results were then compared to THI classes obtained using the measured air temperature and relative humidity data recorded at the stations. Results showed that appreciable percentages of successful forecasts were achieved by the ANN models, indicating therefore that ANNs, when adequately trained, could successfully be used in practical applications of bioclimatic comfort in remote mountainous areas. | |
| 650 | 4 | |a Természettudományok | |
| 650 | 4 | |a Föld- és kapcsolódó környezettudományok | |
| 695 | |a Bioklimatológia - Görögország | ||
| 700 | 0 | 1 | |a Tsiros I. |e aut |
| 700 | 0 | 1 | |a Alvertos N. |e aut |
| 856 | 4 | 0 | |u http://acta.bibl.u-szeged.hu/16936/1/climatologica_044_045_065-071.pdf |z Dokumentum-elérés |