Development and application of artificial neural network models to estimate values of a complex human thermal comfort index associated with urban heat and cool island patterns using air temperature data from a standard meteorological station

  • Konstantinos Moustris
  • Ioannis X. Tsiros
  • Areti Tseliou
  • Panagiotis Nastos
Original Paper

Abstract

The present study deals with the development and application of artificial neural network models (ANNs) to estimate the values of a complex human thermal comfort-discomfort index associated with urban heat and cool island conditions inside various urban clusters using as only inputs air temperature data from a standard meteorological station. The index used in the study is the Physiologically Equivalent Temperature (PET) index which requires as inputs, among others, air temperature, relative humidity, wind speed, and radiation (short- and long-wave components). For the estimation of PET hourly values, ANN models were developed, appropriately trained, and tested. Model results are compared to values calculated by the PET index based on field monitoring data for various urban clusters (street, square, park, courtyard, and gallery) in the city of Athens (Greece) during an extreme hot weather summer period. For the evaluation of the predictive ability of the developed ANN models, several statistical evaluation indices were applied: the mean bias error, the root mean square error, the index of agreement, the coefficient of determination, the true predictive rate, the false alarm rate, and the Success Index. According to the results, it seems that ANNs present a remarkable ability to estimate hourly PET values within various urban clusters using only hourly values of air temperature. This is very important in cases where the human thermal comfort-discomfort conditions have to be analyzed and the only available parameter is air temperature.

Keywords

Urban microclimate Thermal sensation Thermal climate indices Physiologically Equivalent Temperature (PET) index Neural network architecture Performance criteria 

Notes

Acknowledgments

The authors thank the three anonymous reviewers for helpful comments and suggestions that led to a significant improvement of the original manuscript. The authors wish to dedicate this work to the memory of their late colleagues, mentors, and friends Athanasios Paliatsos and Ioannis Ziomas who passed away recently.

References

  1. Arya SP (2001) Introduction to micrometeorology, 2nd edn. Academic Press, Cambridge ISBN 0-12-059354-8Google Scholar
  2. Bilgili M, Sahin B, Yasar A (2007) Application of artificial neural networks for the wind speed prediction of target station using reference stations data. Renew Energ 32:2350–2360Google Scholar
  3. Chronopoulos K, Tsiros IX, Dimopoulos I, Alvertos N (2008) An application of artificial neural network models to estimate air temperature data in areas with sparse network of meteorological stations. J Environ Sci Health A 43:1752–1757CrossRefGoogle Scholar
  4. Cohen P, Potchter O, Matzarakis A (2012) Daily and seasonal climatic conditions of green urban open spaces in the Mediterranean climate and their impact on human comfort. Build Environ 51:285–295CrossRefGoogle Scholar
  5. De Dear RJ, Brager GS (2001) The adaptive model of thermal comfort and energy conservation in the built environment. Int J Biometeorol 45:100–108CrossRefGoogle Scholar
  6. Founda D, Giannakopoulos C (2009) The exceptionally hot summer of 2007 in Athens, Greece. A typical summer in the future climate? Glob Planet Chang 67:227–236CrossRefGoogle Scholar
  7. Giles BD, Balafoutis C, Maheras P (1990) Too hot for comfort: the heatwaves in Greece in 1987 and 1988. Int J Biometeorol 34:98–104CrossRefGoogle Scholar
  8. Grinn-Gofroń A, Strzelczak A (2009) Hourly predictive artificial neural network and multivariate regression tree models of Alternaria and Cladosporium spores in Szczecin (Poland). Int J Biometeorol 53:555–562CrossRefGoogle Scholar
  9. Höppe P (1999) The physiological equivalent temperature—a universal index for the biometeorological assessment of the thermal environment. Int J Biometeorol 43:71–75CrossRefGoogle Scholar
  10. Johansson E, Emmanuel (2006) The influence of urban design on outdoor thermal comfort in the hot, humid city of Colombo, Sri Lanka. Int J Biometeorol 51:119–133CrossRefGoogle Scholar
  11. Laaboudi A, Mouhouche B, Draoui B (2012) Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions. Int J Biometeorol 56:831–841.  https://doi.org/10.1007/s00484-011-0485-7 CrossRefGoogle Scholar
  12. Lin TP (2009) Thermal sensation, adaptation and attendance in a public square in hot and humid regions. Build Environ 44:2017–2026CrossRefGoogle Scholar
  13. Lin TP, Matzarakis A (2008) Tourism climate and thermal comfort in Sun Moon Lake, Taiwan. Int J Biometeorol 52:281–290CrossRefGoogle Scholar
  14. Lin TP, Matzarakis A, Hwang RL (2010) Shading effects on long-term outdoor thermal comfort. Build Environ 45:213–221CrossRefGoogle Scholar
  15. Matzarakis A, Mayer H, Iziomon M (1999) Applications of a universal thermal index: physiological equivalent temperature. Int J Biometeorol 43:76–84CrossRefGoogle Scholar
  16. Matzarakis A, Rutz F, Mayer H (2007) Modelling radiation fluxes in simple and complex environments—application of the RayMan model. Int J Biometeorol 51(4):323–334Google Scholar
  17. Matzarakis A, Rutz F, Mayer H (2010) Modelling radiation fluxes in simple and complex environments: basics of the RayMan model. Int J Biometeorol 54:131–139CrossRefGoogle Scholar
  18. Moustris KP, Tsiros IX, Ziomas IC, Paliatsos AG (2009) Artificial neural network models as a useful tool to forecast human thermal comfort using microclimatic and bioclimatic data in the great Athens area (Greece). J Environ Sci Health A 45(4):447–453CrossRefGoogle Scholar
  19. Moustris KP, Ziomas IC, Paliatsos AG (2010) 3-Day-ahead forecasting of regional pollution index for the pollutants NO2, CO, SO2 and O3 using artificial neural networks in Athens, Greece. Water Air Soil Pollut 209:29–43CrossRefGoogle Scholar
  20. Nastos PT, Matzarakis A (2013) Human bioclimatic conditions, trends, and variability in the Athens University Campus, Greece. Adv Meteorol 2013:8.  https://doi.org/10.1155/2013/976510 Google Scholar
  21. Nastos P, Moustris K, Larissi I, Paliatsos A (2011) Air quality and bioclimatic conditions within the Greater Athens Area, Greece—development and applications of artificial neural networks. In: Nejadkoorki F (ed) Advanced air pollution. InTech-Open Access Publisher, pp 557–584 (ISBN: 978-953-307-511-2)Google Scholar
  22. Niachou K, Livada I, Santamouris M (2008) Experimental study of temperature and airflow distribution inside an urban street canyon during hot summer weather conditions-part I: air and surface temperatures. Build Environ 43:1383–1392CrossRefGoogle Scholar
  23. Pantavou K, Lykoudis S (2014) Modeling thermal sensation in a Mediterranean climate—a comparison of linear and ordinal models. Int J Biometeorol 58:1355–1368.  https://doi.org/10.1007/s00484-013-0737-9 CrossRefGoogle Scholar
  24. Pantavou K, Theoharatos G, Santamouris M, Asimakopoulos D (2013) Outdoor thermal sensation of pedestrians in a Mediterranean climate and a comparison with UTCI. Build Environ 66:82–95CrossRefGoogle Scholar
  25. Papanastasiou DK, Melas D, Kioutsioukis I (2007) Development and assessment of neural network and multiple regression models in order to predict PM10 levels in a medium-sized Mediterranean city. Water Air Soil Pollut 182:325–334CrossRefGoogle Scholar
  26. Prezerakos NG (1989) A contribution to the study of the extreme heatwave over the South Balkans in July 1987. Meteorog Atmos Phys 41:261–271CrossRefGoogle Scholar
  27. Puc M (2012) Artificial neural network model of the relationship between Betula pollen and meteorological factors in Szczecin (Poland). Int J Biometeorol 56:395–401.  https://doi.org/10.1007/s00484-011-0446-1 CrossRefGoogle Scholar
  28. Schlink U, Dorling S, Pelikan E, Nunnari G, Cawley G, Junninen H, Greig A, Foxall R, Eben K, Chatterton T, Vondracek J, Richter M, Dostal M, Bertucco L, Kolehmainen M, Doyle M (2003) A rigorous inter-comparison of ground-level ozone predictions. Atmos Environ 37:3237–3253CrossRefGoogle Scholar
  29. Shashua-Bar L, Tsiros IX, Hoffman M (2010) A modeling study for evaluating the thermal regime of passive cooling scenarios in urban streets. Case study: Athens, Greece. Build Environ 45:2798–2807CrossRefGoogle Scholar
  30. Shashua-Bar L, Tsiros IX, Hoffman ME (2012) Passive cooling design options to ameliorate thermal comfort in urban streets of a Mediterranean climate (Athens) under hot summer conditions. Build Environ 57:110–119CrossRefGoogle Scholar
  31. Sivapragasam C, Arun VM, Giridhar D (2010) A simple approach for improving spatial interpolation of rainfall using ANN. Meteorog Atmos Phys 109:1–7CrossRefGoogle Scholar
  32. Theoharatos G, Pantavou K, Mavrakis A, Spanou A, Katavoutas G, Efstathiou P, Mpekas P, Asimakopoulos D (2010) Heat waves observed in 2007 in Athens, Greece: synoptic conditions, bioclimatological assessment, air quality levels and health effects. Environ Res 110(2):152–161.  https://doi.org/10.1016/j.envres.2009.12.002 CrossRefGoogle Scholar
  33. Tseliou A, Tsiros IX, Lykoudis S, Nikolopoulou M (2010) An evaluation of three biometeorological indices for human thermal comfort in urban outdoor areas under real climatic conditions. Build Environ 45:1346–1352CrossRefGoogle Scholar
  34. Tseliou A, Tsiros IX, Nikolopoulou M (2017) Seasonal differences in thermal sensation in the outdoor urban environment of Mediterranean climates—the example of Athens, Greece. Int J Biometeorol 61:1209–1220CrossRefGoogle Scholar
  35. Tsiros IX, Hoffman ME (2014) Thermal and comfort conditions in a rear wooded garden and its adjacent semi-open spaces in a Mediterranean climate (Athens) during summer. Archit Sci Rev 57:63–82CrossRefGoogle Scholar
  36. Tsiros IX, Dimopoulos IF, Chronopoulos K, Chronopoulos G (2009) Estimating airborne pollutant concentrations in vegetated urban sites using statistical models with microclimate and urban geometry parameters as predictor variables: a case study in the city of Athens. J Environ Sci Health A 44:1496–1502CrossRefGoogle Scholar
  37. Werbos P (1988) Generalization of backpropagation with application to a recurrent gas market model. Neural Netw 1:339–356CrossRefGoogle Scholar
  38. Zounemat-Kermani M (2012) Hourly predictive Levenberg-Marquardt ANN and multi linear regression models for predicting of dew point temperature. Meteorog Atmos Phys 117:181–192CrossRefGoogle Scholar

Copyright information

© ISB 2018

Authors and Affiliations

  • Konstantinos Moustris
    • 1
  • Ioannis X. Tsiros
    • 2
  • Areti Tseliou
    • 3
  • Panagiotis Nastos
    • 4
  1. 1.Department of Mechanical EngineeringPiraeus University of Applied SciencesAthensGreece
  2. 2.Meteorology LaboratoryAgricultural University of AthensAthensGreece
  3. 3.College of Natural and Health SciencesZayed UniversityDubaiUnited Arab Emirates
  4. 4.Climatology LaboratoryNational and Kapodestrian University of AthensAthensGreece

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