Neural network model for maximum ozone concentration prediction

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dc.catalogadorgjm
dc.contributor.authorAcuña, Gonzalo
dc.contributor.authorJorquera, Héctor
dc.contributor.authorPérez, Ricardo
dc.date.accessioned2024-05-30T16:23:23Z
dc.date.available2024-05-30T16:23:23Z
dc.date.issued1996
dc.description.abstractA neural network dynamic model was used for predicting maximum ozone (O3) concentration at Santiago de Chile. Learning and test data were collected during summer and springtime periods of 1990, 1992 and 1993. A neural network having O3 t, Tt+1 (maximum air temperature) and Tt as inputs for predicting O3 t+1 was chosen because of its low test error. This neural network model greatly reduces the error coming from a pure persistence model when applied to the generalization set of data (1994). Long-term predictions results confirm the good concordance obtained between the observed and forecasted values thus showing the adequacy of neural networks to model the dynamics of this complex environmental phenomena.
dc.fuente.origenORCID
dc.identifier.doi10.1007/3-540-61510-5_47
dc.identifier.urihttps://doi.org/10.1007/3-540-61510-5_47
dc.identifier.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-0342971642&partnerID=MN8TOARS
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86064
dc.information.autorucEscuela de Ingeniería; Jorquera, Héctor; 0000-0002-7462-7901; 100302
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final268
dc.pagina.inicio263
dc.relation.ispartofLecture Notes in Computer Science
dc.rightsacceso restringido
dc.subjectNeural networks
dc.subjectOzone
dc.subjectForecasting
dc.subjectDynamic modeling
dc.subjectPredictive model
dc.subject.ddc600
dc.subject.deweyTecnologíaes_ES
dc.titleNeural network model for maximum ozone concentration prediction
dc.typecomunicación de congreso
dc.volumen1112
sipa.codpersvinculados100302
sipa.trazabilidadORCID;2024-05-27
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