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dc.contributor.authorGutiérrez Torre, Alberto
dc.contributor.authorBerral García, Josep Lluís
dc.contributor.authorBuchaca Prats, David
dc.contributor.authorGuevara Vilardell, Marc
dc.contributor.authorSoret, Albert
dc.contributor.authorCarrera Pérez, David
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2020-09-23T18:24:49Z
dc.date.issued2020-07-07
dc.identifier.citationGutiérrez-Torre, A. [et al.]. Improving maritime traffic emission estimations on missing data with CRBMs. "Engineering applications of artificial intelligence", 7 Juliol 2020, vol. 94, p. 103793:1-103793:10.
dc.identifier.issn0952-1976
dc.identifier.otherhttps://arxiv.org/pdf/2009.03001.pdf
dc.identifier.urihttp://hdl.handle.net/2117/329194
dc.description© Elsevier 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.abstractMaritime traffic emissions are a major concern to governments as they heavily impact the Air Quality in coastal cities. Ships use the Automatic Identification System (AIS) to continuously report position and speed among other features, and therefore this data is suitable to be used to estimate emissions, if it is combined with engine data. However, important ship features are often inaccurate or missing. State-of-the-art complex systems, like CALIOPE at the Barcelona Supercomputing Center, are used to model Air Quality. These systems can benefit from AIS based emission models as they are very precise in positioning the pollution. Unfortunately, these models are sensitive to missing or corrupted data, and therefore they need data curation techniques to significantly improve the estimation accuracy. In this work, we propose a methodology for treating ship data using Conditional Restricted Boltzmann Machines (CRBMs) plus machine learning methods to improve the quality of data passed to emission models that can also be applied to other GPS and time-series problems. Results show that we can improve the default methods proposed to cover missing data. In our results, we observed that using our method the models boosted their accuracy to detect otherwise undetectable emissions. In particular, we used a real data-set of AIS data, provided by the Spanish Port Authority, to estimate that thanks to our method, the model was able to detect 45% of additional emissions, representing 152 tonnes of pollutants per week in Barcelona and propose new features that may enhance emission modeling.
dc.description.sponsorshipThis project hasreceived funding from the European Research Council (ERC) under the European Union Horizon 2020 research and innovation programme (grant agreement No 639595). This work is partially supported by the Ministry of Economy, Industry and Competitiveness of Spain under contracts TIN2015-65316-P,2014SGR1051, IJCI2016-27485, and Severo Ochoa Center of Excellence SEV-2015-0493-16-5.
dc.language.isoeng
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subjectÀrees temàtiques de la UPC::Nàutica::Impacte ambiental
dc.subject.lcshArtificial intelligence
dc.subject.lcshNavigation -- Environmental aspects
dc.subject.otherData cleaning
dc.subject.otherAIS
dc.subject.otherEmission modeling
dc.subject.otherCRBM
dc.subject.otherShip time series
dc.subject.otherGPS
dc.titleImproving maritime traffic emission estimations on missing data with CRBMs
dc.typeArticle
dc.subject.lemacIntel·ligència artificial
dc.subject.lemacNavegació -- Aspectes ambientals
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.identifier.doi10.1016/j.engappai.2020.103793
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/abs/pii/S0952197620301822?via%3Dihub
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac28932415
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/639595/EU/Holistic Integration of Emerging Supercomputing Technologies/Hi-EST
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TIN2015-65316-P
dc.relation.projectidinfo:eu-repo/grantAgreement/AGAUR/PRI2010-2013/2014 SGR 1051
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/SEV-2015-0493
dc.date.lift2022-07-08
local.citation.authorGutiérrez-Torre, A.; Berral, J.; Buchaca, D.; Guevara, M.; Soret, A.; Carrera, D.
local.citation.publicationNameEngineering applications of artificial intelligence
local.citation.volume94
local.citation.startingPage103793:1
local.citation.endingPage103793:10
local.requestitem.embargattrue


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