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dc.contributor.authorRuiz Aguilar, Juan Jesús
dc.contributor.authorMoscoso López, José Antonio
dc.contributor.authorUrda Muñoz, Daniel 
dc.contributor.authorGonzález Enrique, Francisco Javier
dc.contributor.authorTurias Domínguez, Ignacio J.
dc.date.accessioned2023-01-17T11:46:58Z
dc.date.available2023-01-17T11:46:58Z
dc.date.issued2020-11
dc.identifier.urihttp://hdl.handle.net/10259/7250
dc.description.abstractAn accurate prediction of freight volume at the sanitary facilities of seaports is a key factor to improve planning operations and resource allocation. This study proposes a hybrid approach to forecast container volume at the sanitary facilities of a seaport. The methodology consists of a three-step procedure, combining the strengths of linear and non-linear models and the capability of a clustering technique. First, a self-organizing map (SOM) is used to decompose the time series into smaller clusters easier to predict. Second, a seasonal autoregressive integrated moving averages (SARIMA) model is applied in each cluster in order to obtain predicted values and residuals of each cluster. These values are finally used as inputs of a support vector regression (SVR) model together with the historical data of the cluster. The final prediction result integrates the prediction results of each cluster. The experimental results showed that the proposed model provided accurate prediction results and outperforms the rest of the models tested. The proposed model can be used as an automatic decision-making tool by seaport management due to its capacity to plan resources in advance, avoiding congestion and time delays.en
dc.description.sponsorshipThis research was funded by MICINN (Ministerio de Ciencia e Innovación-Spain), grant number RTI2018-098160-B-I00.en
dc.format.mimetypeapplication/pdf
dc.language.isoengen
dc.publisherMDPIes
dc.relation.ispartofApplied sciences. 2020, V. 10, n. 23, e8326es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMaritime transporten
dc.subjectContainer forecastingen
dc.subjectSupport vector regressionen
dc.subjectSelf-organizing mapsen
dc.subjectMachine learningen
dc.subjectHybrid modelsen
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleA Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilitiesen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.3390/app10238326es
dc.identifier.doi10.3390/app10238326
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098160-B-I00/ES/DEEP LEARNING IN AIR POLLUTION FORECASTING/en
dc.identifier.essn2076-3417
dc.journal.titleApplied Sciencesen
dc.volume.number10es
dc.issue.number23es
dc.type.hasVersioninfo:eu-repo/semantics/draftes


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