Table of Contents
ISRN Meteorology
Volume 2013 (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.

Linked References

  1. M. Santamouris, G. Mihalakakou, N. Papanikolaou, and D. N. Asimakopoulos, “A neural network approach for modeling the heat island phenomenon in urban areas during the summer period,” Geophysical Research Letters, vol. 26, no. 3, pp. 337–340, 1999. View at Google Scholar · View at Scopus
  2. P. Cohen, O. Potchter, and A. Matzarakis, “Daily and seasonal climatic conditions of green urban open spaces in the Mediterranean climate and their impact on human comfort,” Building and Environment, vol. 51, pp. 285–295, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. H. E. Landsberg, The Urban Climate, Academic Press, New York, NY, USA, 1981.
  4. L. Cui and J. Shi, “Urbanization and its environmental effects in Shanghai, China,” Urban Climate, vol. 2, pp. 1–15, 2012. View at Publisher · View at Google Scholar
  5. R. J. Barry and R. J. Chorley, Atmosphere, Weather and Climate, Routledge, Taylor & Francis Group, London, UK, 2001.
  6. K. Chronopoulos, A. Kamoutsis, A. Matsoukis, and E. Manoli, “An artificial neural network model application for the estimation of thermal comfort conditions in mountainous regions, Greece,” Atmosfera, vol. 25, no. 2, pp. 171–181, 2012. View at Google Scholar · View at Scopus
  7. C. D. Whiteman, Mountain Meteorology, Oxford University Press, New York, NY, USA, 2000.
  8. A. D. Richardson, X. Lee, and A. J. Friedland, “Microclimatology of treeline spruce-fir forests in mountains of the northeastern United States,” Agricultural and Forest Meteorology, vol. 125, no. 1-2, pp. 53–66, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Matsoukis, A. Kamoutsis, and A. Chronopoulou-Sereli, “Air temperature and thermal comfort conditions in mountainous and urban regions,” International Journal of Sustainable Development and Planning, vol. 4, no. 4, pp. 357–363, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. S. K. Nepal and R. Chipeniuk, “Mountain tourism: toward a conceptual framework,” Tourism Geographies, vol. 7, no. 3, pp. 313–333, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Horbert, A. Kirchgeorg, A. Chronopoulou-Sereli, and J. Chronopoulos, Impact of Green on the Urban Atmosphere in Athens, Scientific Series of the International Bureau, Kernforschungsanlage Jülich GmbH, Berlin, Germany, 1988.
  12. M. Guler, B. Cemek, and H. Gunal, “Assessment of some spatial climatic layers through GIS and statistical analysis techniques in Samsum Turkey,” Meteorological Applications, vol. 14, no. 2, pp. 163–169, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Jolly, T. Brossard, H. Cardot, J. Cavailhes, M. Hilal, and P. Wavresky, “Temperature interpolation based on local information: the example of France,” International Journal of Climatology, vol. 31, no. 14, pp. 2141–2153, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. D. B. Shank, G. Hoogenboom, and R. W. McClendon, “Dewpoint temperature prediction using artificial neural networks,” Journal of Applied Meteorology and Climatology, vol. 47, no. 6, pp. 1757–1769, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. K. I. Chronopoulos, I. X. Tsiros, I. F. Dimopoulos, and N. Alvertos, “An application of artificial neural network models to estimate air temperature data in areas with sparse network of meteorological stations,” Journal of Environmental Science and Health A, vol. 43, no. 14, pp. 1752–1757, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. O. Kisi and J. Shiri, “Prediction of long-term air temperature using geographical inputs,” International Journal of Climatology, 2013. View at Google Scholar
  17. P. A. Vouterakos, K. P. Moustris, A. Bartzokas, I. C. Ziomas, P. T. Nastos, and A. G. Paliatsos, “Forecasting the discomfort levels within the greater Athens area, Greece using artificial neural networks and multiple criteria analysis,” Theoretical and Applied Climatology, vol. 110, no. 3, pp. 329–343, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. B. Smith, G. Hoogenboom, and R. W. McClendon, “Artificial neural networks for automated year-round temperature prediction,” Computers and Electronics in Agriculture, vol. 68, no. 1, pp. 52–61, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Kamoutsis, A. Matsoukis, K. Chronopoulos, and E. Manoli, “A comparative study of human thermal comfort conditions in two mountainous regions in Greece during summer,” Global Nest Journal, vol. 12, no. 4, pp. 401–408, 2010. View at Google Scholar · View at Scopus
  20. K. Chronopoulos, A. Kamoutsis, and A. Matsoukis, “Thermal comfort estimation in relation to different orientation in mountainous regions in Greece by using artificial neural networks,” Global Nest Journal, vol. 14, no. 4, pp. 532–539, 2012. View at Google Scholar
  21. Institute of Environmental Research and Sustainable Development (IERSD), “National Observatory of Athens,” Climatological means, 2013, http://www.meteo.noa.gr/ENG/iersd_climat-table.htm.
  22. A. Flocas, Lessons in Meteorology and Climatology, Zitis Publications, Thessaloniki, Greece, 2nd edition, 1997.
  23. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. View at Google Scholar · View at Scopus
  24. S. E. Fahlman, “Faster-learning variations on back-propagation: an empirical study,” in Proceedings of the 1988 Connectionist Models Summer School, pp. 38–51, Morgan Kaufmann Publishers, San Mateo, Calif, USA, 1988. View at Google Scholar
  25. L. Fausett, Fundamentals of Neural Networks, Prentice Hall, New York, NY, USA, 1994.