@article {4967,
	title = {Forecasting the discomfort levels within the greater Athens area, Greece using artificial neural networks and multiple criteria analysis},
	journal = {Theoretical and Applied Climatology},
	volume = {110},
	year = {2012},
	note = {Cited By :7Export Date: 2 November 2015Correspondence Address: Nastos, P. T.; Laboratory of Climatology and Atmospheric Environment, Faculty of Geology and Geoenvironment, University of Athens, Panepistimiopolis, 157-84 Athens, Greece; email: nastos@geol.uoa.grReferences: Antonic, O., Hatic, D., Krian, J., Bukocev, D., Modelling groundwater regime acceptable for the forest survival after the building of the hydro-electric power plant (2001) Ecol Model, 138, pp. 277-288;Atthajariyakul, S., Leephakpreeda, T., Neural computing thermal comfort index for HVAC systems (2005) Energ Convers Manage, 46, pp. 2553-2565; 
Becker, S., Potchter, O., Yaakov, Y., Calculated and observed human thermal sensation in an extremely hot and dry climate (2003) Energ Buildings, 35, pp. 747-756; 
Besancenot, J.P., Le bioclimat humain de Rio (1978) Recherches De Climatologie En Milieu Tropical Et Mediterranean, , J. B. Suchel, E. Altes, J. P. Besancenot, and P. Maheras (Eds.), Dijon: Universite de Dijon, Cahier No. 6 Du Centre De Recherches De Climatologie; 
Bishop, C., (1995) Neural Networks for Pattern Recognition, , Oxford: Oxford University Press; 
Ceravolo, F., de Felice, M., Pizzuti, S., Combining back-propagation and genetic algorithms to train neural networks for ambient temperature modelling in Italy (2009) Appl EvolComput, 5484, pp. 123-131. , doi:10.1007/978-3-642-01129-0_16; 
Chronopoulos, K.I., Tsiros, I.X., Dimopoulos, I.F., Alvertos, N., An application of artificial neural network models to estimate air temperature data in areas with sparse network of meteorological stations (2008) J Environ Sci Health A Tox Hazard Subst Environ Eng, 43, pp. 1752-1757; 
Chronopoulos, K.I., Tsiros, I.X., Alvertos, N., Assessment of bioclimatic comfort using artificial neural network models-a preliminary study in a remote mountainous area of southern Greece (2011) Acta Climatologica Et Chorologica, 44-45, pp. 65-71; 
Cohen, P., Oded Potchter, O., Matzarakis, A., Daily and seasonal climatic conditions of green urban open spaces in the Mediterranean climate and their impact on human comfort (2012) Build Environ, 51, pp. 285-295; 
Comrie, A.C., Comparing neural networks and regression models for ozone forecasting (1997) J Air Waste Manage, 47, pp. 653-663; 
Conti, S., Meli, P., Menelli, G., Solimini, R., Toccaceli, V., Vichi, M., Beltrano, C., Perini, L., Epidemiologic study of mortality during the summer 2003 heat wave in Italy (2005) Environ Res, 98, pp. 390-399; 
D{\'\i}az, J., Garcia, R., de Velazquez, C.F., Hernandez, E., Lopez, C., Otero, A., Effects of extremely hot days on people older than 65 years in Seville (Spain) from 1986 to 1997 (2002) Int J Biometeorol, 46 (3), pp. 145-149; 
Dreyfus, G., (2005) Neural Networks: Methodology and Applications, , Berlin: Springer; 
Elbir, T., Comparison of model predictions with the data of an urban air quality monitoring network in Izmir, Turkey (2003) Atmos Environ, 37, pp. 2149-2157; 
Fiala, D., Havenith, G., Br{\"o}de, P., Kampmann, B., Jendritzky, G., UTCI-Fiala multi-node model of human heat transfer and temperature regulation (2011) Int J Biometeorol., , doi: 10. 1007/s00484-011-0424-7; 
Giles, B.D., Balafoutis, C.H., Maheras, P., Too hot for comfort: the heatwaves in Greece in 1987 and 1988 (1990) Int J Biometeorol, 34, pp. 98-104; 
Gobakis, K., Kolokotsab, D., Synnefac, A., Saliari, M., Giannopoulou, K., Santamouris, M., Development of a model for urban heat island prediction using neural network techniques (2011) Sustain Cities Soc, 1, pp. 104-115; 
Hajat, S., Kosatky, T., Heat-related mortality: a review and exploration of heterogeneity (2010) J Epidemiol Community Health, 64, pp. 753-760; 
H{\"o}ppe, P.R., The physiological equivalent temperature-a universal index for the biometeorological assessment of the thermal environment (1999) Int J Biometeorol, 43, pp. 71-75; 
Jendritzky, G., Maarouf, A., Fiala, D., Staiger, H., An update on the development of a universal thermal climate index (2002) 15th Conf. Biomet. Aerobiol., pp. 129-133. , And 16th ICB02, 27 Oct-1 Nov 2002, Kansas City, AMS; 
Jiang, Y., Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models (2008) Energ Policy, 36, pp. 3833-3837; 
Karul, C., Soyupak, S., Cilesiz, A.F., Akbay, N., Germen, E., Case studies on the use of neural networks in eutrophication modeling (2000) Ecol Model, 134, pp. 145-152; 
Kostopoulou, E., Giannakopoulos, C., Anagnostopoulou, C., Tolika, K., Maheras, P., Vafiadis, M., Founda, D., Simulating maximum and minimum temperature over Greece: a comparison of three downscaling techniques (2007) Theor Appl Climatol, 90, pp. 65-82; 
Ma, C.C.Y., Iqbal, M., Statistical comparison of models for estimating solar radiation on inclined surgaces (1983) Sol Energy, 31, pp. 313-317; 
Matzarakis, A., Mayer, H., The extreme heat wave in Athens in July 1987 from the point of view of human biometeorology (1991) Atmos Environ, 25 (2), pp. 203-211; 
Matzarakis, A., Nastos, P.T., Human-biometeorological assessment of heat waves in Athens (2010) Theor Appl Climatol, 105 (1), pp. 99-106; 
Matzarakis, A., Mayer, H., Iziomon, M., Applications of a universal thermal index: physiological equivalent temperature (1999) Int J Biometeorol, 43, pp. 76-84; 
Mayer, H., H{\"o}ppe, P., Thermal comfort of man in different urban environments (1987) Theor Appl Climatol, 38, pp. 3-49; 
McGregor, G.R., Markou, M.T., Bartzokas, A., Katsoulis, B.D., An evaluation of the nature and timing of summer human thermal discomfort in Athens, Greece (2002) Clim Res, 20, pp. 83-94; 
Mendoza, G.A., Macou, P., Prahbu, R., Sukadri, D., Purnomo, H., Hartanto, H., (1999) Guidelines for applying multi-criteria analysis to the assessment of criteria and indicators, , http://www.cifor.cgiar.org/acm/methods/toolbox9.html, Criteria \& Indicators Toolbox series No. 9. Jakarta, Indonesia: CIFOR. Available at; 
(2011) Hydrological Observatory of Athens, , http://hoa.ntua.gr/info, METEONET, Available at; 
Mihalakakou, G., Flocas, H.A., Santamouris, M., Helmis, C.G., Application of neural networks to the simulation of the heat island over Athens, Greece, using synoptic types as a predictor (2002) J Appl Meteorol, 41 (5), pp. 519-527; 
Moon, J.W., Kim, J.J., ANN-based thermal control models for residential buildings (2010) Build Environ, 45, pp. 1612-1625; 
Moon, J.W., Jung, S.K., Kim, J.J., Application of ANN (artificial-neural-network) in residential thermal control (2009) In: Eleventh International IBPSA Conference, pp. 64-71. , Building Simulation 2009. Glasgow, Scotland, 27-30 July, 2009; 
Moustris, K.P., Ziomas, I.C., Paliatsos, A.G., 24 Hours in advance forecasting of thermal comfort-discomfort levels during the hot period of the year at representative locations of Athens city, Greece (2009) Fresen Environ Bull, 18 (5), pp. 601-608; 
Moustris, K.P., Tsiros, I.X., Ziomas, I.C., Paliatsos, A.G., Artificial neural network models as a useful tool to forecast human thermal comfort using microclimatic and bioclimatic data in the great Athens area (Greece) (2010) J Environ Sci Heal A, 45, pp. 447-453; 
Moustris, K.P., Ziomas, I.C., Paliatsos, A.G., 3-Day-ahead forecasting of regional pollution index for the pollutants NO2, CO, SO2, and O3 using artificial neural networks in Athens, Greece (2010) Water Air Soil Poll, 209, pp. 29-43; 
Muthers, S., Matzarakis, A., Koch, E., Summer climate and mortality in Vienna-a human biometeorological approach of heat-related mortality during the heat waves in 2003 (2010) Wien Klin Wochenschr, 122, pp. 525-531; 
Nastos, P.T., Matzarakis, Weather impacts on respiratory infections in Athens, Greece (2006) Int J Biometeorol, 50, pp. 358-369; 
Nastos, P.T., Matzarakis, A., Human-biometeorological effects on sleep disturbances in Athens, Greece: a preliminary evaluation (2008) Indoor Built Environ, 17 (6), pp. 535-542; 
Nastos, P.T., Matzarakis, Variability of tropical days over Greece within the second half of the twentieth century (2008) Theor Appl Climatol, 93, pp. 75-89; 
Nastos, P.T., Matzarakis, A., The effect of air temperature and human thermal indices on mortality in Athens, Greece (2011) Theor Appl Climatol., , doi: 10. 1007/s00704-011-0555-0; 
Nunnari, G., Dorling, S., Schlink, U., Cawley, C., Foxall, R., Chatterton, T., Modelling SO2 concentration at a point with statistical approaches (2004) Environ Modell Softw, 19, pp. 887-905; 
Paliatsos, A.G., Nastos, P.T., Relation between air pollution episodes and discomfort index in the greater Athens area, Greece (1999) Global Nest J, 1, pp. 91-97; 
Papanastasiou, D., Melas, D., Kioutsioukis, I., Development and assessment of neural network and multiple regression models in order to predict PM10 levels in a medium-sized Mediterranean city (2007) Water Air Soil Poll, 182, pp. 325-334; 
Pelliccioni, A., Cotroneo, R., Pungi, F., (2010) Optimization of neural net training using patterns selected by cluster analysis: A case-study of ozone prediction level, , http://ams.confex.com/ams/90annual/techprogram/session_23974.htm, Eighth conference on Artificial Intelligence and its Applications to the Environmental Sciences, AMS 90th Annual Meeting, Atlanta, Georgia, 17-21 January, 2010. Available at; 
Philandras, C.M., Metaxas, D.A., Nastos, P.T., Repapis, C.C., Climate variability and urbanization in Athens (1999) Theor Appl Climatol, 63, pp. 65-72; 
Santamouris, M., Mihalakakou, G., Papanikolaou, N., Asimakopoulos, D.N., A neural network approach for modeling the heat island phenomenon in urban areas during the summer period (1999) Geophys Res Lett, 26 (3), pp. 337-340; 
Shahi, A., Atan, R.B., Sulaiman, M.N., Detecting effectiveness of outliers and noisy data on fuzzy system using FCM (2009) Eur J Sci Res, 36, pp. 627-638; 
Shimizu, Y., Jindo, T., A fuzzy logic analysis method for evaluating human sensitivities (1995) Int J Ind Ergonom, 15, pp. 39-47; 
Siple, P.A., Passel, C.F., Measurements of dry atmospheric cooling in subfreezing temperatures (1945) Proc Am Philos Soc, 89, pp. 177-199; 
Spagnolo, J., de Dear, R., A field study of thermal comfort in outdoor and semi-outdoor environments in subtropical Sydney Australia (2003) Build Environ, 38, pp. 721-738; 
Theoharatos, G., Pantavou, K., Mavrakis, A., Spanou, A., Katavoutas, G., Efstathiou, P., Mpekas, P., Asimakopoulos, D., Heat waves observed in 2007 in Athens, Greece: Synoptic conditions, bioclimatological assessment, air quality levels and health effects, Greece (2010) Environ Res, 110, pp. 152-161; 
Thom, E.C., The discomfort index (1959) Weatherwise, 12, pp. 57-60; 
Tseliou, A., Tsiros, I.X., Lykoudis, S., Nikolopoulou, M., An evaluation of three biometeorological indices for human thermal comfort in urban outdoor areas under real climatic conditions (2010) Build Environ, 45, pp. 1346-1352; 
Tzenkova, A.S., Kandjov, I.M., Ivancheva, J.N., Some biometeorological aspects of urban climate in Sofia (2003) Proc of Fifth Int Conf Urban Climate, Lodz, Poland, 2, pp. 103-106; 
Velten, K., (2009) Mathematical Modeling and Simulation: Introduction for Scientists and Engineers, , Germany: Wiley; 
Yang, I.H., Kim, K.W., Prediction of the time of room air temperature descending for heating systems in buildings (2004) Build Environ, 39, pp. 19-29
},
	month = {2012},
	pages = {329 - 343},
	abstract = {In this work, artificial neural networks (ANNs) were developed and applied in order to forecast the discomfort levels due to the combination of high temperature and air humidity, during the hot season of the year, in eight different regions within the Greater Athens area (GAA), Greece. For the selection of the best type and architecture of ANNs-forecasting models, the multiple criteria analysis (MCA) technique was applied. Three different types of ANNs were developed and tested with the MCA method. Concretely, the multilayer perceptron, the generalized feed forward networks (GFFN), and the time-lag recurrent networks were developed and tested. Results showed that the best ANNs type performance was achieved by using the GFFN model for the prediction of discomfort levels due to high temperature and air humidity within GAA. For the evaluation of the constructed ANNs, appropriate statistical indices were used. The analysis proved that the forecasting ability of the developed ANNs models is very satisfactory at a significant statistical level of p < 0. 01. {\textcopyright} 2012 Springer-Verlag.},
	keywords = {air temperature, artificial neural network, Greece, humidity, performance assessment, statistical analysis, weather forecasting},
	isbn = {0177798X (ISSN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870290136\&partnerID=40\&md5=edc06f4c8955c1f54bd255e6b998ba37},
	author = {Vouterakos, P. A. and Moustris, K. P. and Bartzokas, A. and Ziomas, I. C. and Nastos, P. T. and Paliatsos, A. G.}
}
