Publication:
Forecasting high waters at Venice Lagoon using chaotic time series analisys and nonlinear neural netwoks

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Computación Evolutiva y Redes Neuronales (EVANNAI)es
dc.contributor.authorZaldívar, J.M.
dc.contributor.authorGutiérrez, E.
dc.contributor.authorGalván, Inés M.
dc.contributor.authorStrozzi, F.
dc.contributor.authorTomasin, A.
dc.date.accessioned2009-06-08T12:30:42Z
dc.date.available2009-06-08T12:30:42Z
dc.date.issued2000
dc.description.abstractTime series analysis using nonlinear dynamics systems theory and multilayer neural networks models have been applied to the time sequence of water level data recorded every hour at 'Punta della Salute' from Venice Lagoon during the years 1980-1994. The first method is based on the reconstruction of the state space attractor using time delay embedding vectors and on the characterisation of invariant properties which define its dynamics. The results suggest the existence of a low dimensional chaotic attractor with a Lyapunov dimension, DL, of around 6.6 and a predictability between 8 and 13 hours ahead. Furthermore, once the attractor has been reconstructed it is possible to make predictions by mapping local-neighbourhood to local-neighbourhood in the reconstructed phase space. To compare the prediction results with another nonlinear method, two nonlinear autoregressive models (NAR) based on multilayer feedforward neural networks have been developed. From the study, it can be observed that nonlinear forecasting produces adequate results for the 'normal' dynamic behaviour of the water level of Venice Lagoon, outperforming linear algorithms, however, both methods fail to forecast the 'high water' phenomenon more than 2-3 hours ahead.
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationJournal of hydroinformatics, 2000, vol. 2, n.1, p. 61- 84
dc.identifier.issn1464-7141
dc.identifier.publicationfirstpage61
dc.identifier.publicationissue1
dc.identifier.publicationlastpage84
dc.identifier.publicationtitleJournal of hydroinformatics
dc.identifier.publicationvolume2
dc.identifier.urihttps://hdl.handle.net/10016/4370
dc.language.isoeng
dc.publisherIWA Publishing
dc.relation.publisherversionhttp://www.iwaponline.com/jh/002/jh0020061.htm
dc.rights© IWA Publishing
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaInformática
dc.subject.otherForecasting
dc.subject.otherNonlinear neural networks
dc.subject.otherTime series analysis
dc.titleForecasting high waters at Venice Lagoon using chaotic time series analisys and nonlinear neural netwoks
dc.typeresearch article*
dc.type.reviewPeerReviewed
dspace.entity.typePublication
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