Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece

Grivas, G; Chaloulakou, A

dc.contributor.author	Grivas, G	en
dc.contributor.author	Chaloulakou, A	en
dc.date.accessioned	2014-03-01T01:23:38Z
dc.date.available	2014-03-01T01:23:38Z
dc.date.issued	2006	en
dc.identifier.issn	1352-2310	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17058
dc.subject	Air pollution modeling	en
dc.subject	Genetic algorithms	en
dc.subject	Neural networks	en
dc.subject	Particulate matter	en
dc.subject	Prediction	en
dc.subject.classification	Environmental Sciences	en
dc.subject.classification	Meteorology & Atmospheric Sciences	en
dc.subject.other	Air pollution	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Particles (particulate matter)	en
dc.subject.other	Probability	en
dc.subject.other	Regression analysis	en
dc.subject.other	Air pollution modeling	en
dc.subject.other	Linear regression models	en
dc.subject.other	Prediction	en
dc.subject.other	Time-scale input variables	en
dc.subject.other	Particulate emissions	en
dc.subject.other	artificial neural network	en
dc.subject.other	genetic algorithm	en
dc.subject.other	suspended particulate matter	en
dc.subject.other	air monitoring	en
dc.subject.other	air pollution	en
dc.subject.other	article	en
dc.subject.other	artificial neural network	en
dc.subject.other	concentration (parameters)	en
dc.subject.other	genetic algorithm	en
dc.subject.other	Greece	en
dc.subject.other	intermethod comparison	en
dc.subject.other	meteorology	en
dc.subject.other	model	en
dc.subject.other	multiple linear regression analysis	en
dc.subject.other	particulate matter	en
dc.subject.other	prediction	en
dc.subject.other	priority journal	en
dc.subject.other	sporting event	en
dc.subject.other	Athens [Attica]	en
dc.subject.other	Attica	en
dc.subject.other	Eurasia	en
dc.subject.other	Europe	en
dc.subject.other	Greece	en
dc.subject.other	Southern Europe	en
dc.title	Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.atmosenv.2005.10.036	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.atmosenv.2005.10.036	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	The aim of the present work is to evaluate the potential of various developed neural network models to provide reliable predictions of PM10 hourly concentrations, a task that is known to present certain difficulties. The modeling study involves 4 measurement locations within the Greater Athens Area which experiences a significant PM-related air pollution problem. The PM10 data used cover the period of 2001-2002. Artificial neural network models were developed using a combination of meteorological and time-scale input variables. A genetic algorithm optimization procedure for the selection of the input variables was also evaluated. The results of the neural network models were rather satisfactory, with values of the coefficient of determination (r(2)) for independent test sets ranging between 0.50 and 0.67 for the four sites and values of the index of agreement between 0.80 and 0.89. The performance of examined neural network models was superior in comparison with multiple linear regression models that were developed in parallel (r2 for regression models ranging between 0.29 and 0.35). Their performance was also found adequate in the case of high-concentration events, with acceptable probabilities of detection and low false alarm rates. The suitability of the developed neural network models for use at real-time conditions was further evaluated for PM10 hourly concentrations recorded during the days of the 2004 Athens Olympic Games. (c) 2005 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Atmospheric Environment	en
dc.identifier.doi	10.1016/j.atmosenv.2005.10.036	en
dc.identifier.isi	ISI:000236210300004	en
dc.identifier.volume	40	en
dc.identifier.issue	7	en
dc.identifier.spage	1216	en
dc.identifier.epage	1229	en