Forecasting the discomfort levels within the greater Athens area, Greece using artificial neural networks and multiple criteria analysis

Vouterakos, PA; Moustris, KP; Bartzokas, A; Ziomas, IC; Nastos, PT; Paliatsos, AG

dc.contributor.author	Vouterakos, PA	en
dc.contributor.author	Moustris, KP	en
dc.contributor.author	Bartzokas, A	en
dc.contributor.author	Ziomas, IC	en
dc.contributor.author	Nastos, PT	en
dc.contributor.author	Paliatsos, AG	en
dc.date.accessioned	2014-03-01T11:46:59Z
dc.date.available	2014-03-01T11:46:59Z
dc.date.issued	2012	en
dc.identifier.issn	0177798X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/38049
dc.title	Forecasting the discomfort levels within the greater Athens area, Greece using artificial neural networks and multiple criteria analysis	en
heal.type	other	en
heal.identifier.primary	10.1007/s00704-012-0626-x	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00704-012-0626-x	en
heal.publicationDate	2012	en
heal.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. © 2012 Springer-Verlag.	en
heal.journalName	Theoretical and Applied Climatology	en
dc.identifier.doi	10.1007/s00704-012-0626-x	en
dc.identifier.spage	1	en
dc.identifier.epage	15	en