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Multi-objective evolutionary spatio-temporal forecasting of air pollution

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dc.contributor.authorEspinosa Fernández, Raquel
dc.contributor.authorJiménez Barrionuevo, Fernando
dc.contributor.authorPalma Méndez, José Tomás
dc.contributor.departmentIngeniería de la Información y las Comunicaciones
dc.date.accessioned2025-01-18T18:57:01Z
dc.date.available2025-01-18T18:57:01Z
dc.date.issued2022-05-31
dc.description© 2022 The Author(s). This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. This document is the Published version of a Published Work that appeared in final form in Future Generation Computer Systems. To access the final edited and published work see https://doi.org/10.1016/j.future.2022.05.020
dc.description.abstractNowadays, air pollution forecasting modeling is vital to achieve an increase in air quality, allowing an improvement of ecosystems and human health. It is important to consider the spatial characteristics of the data, as they allow us to infer predictions in those areas for which no information is available. In the current literature, there are a large number of proposals for spatio-temporal air pollution forecasting. In this paper we propose a novel spatio-temporal approach based on multi-objective evolutionary algorithms for the identification of multiple non-dominated linear regression models and their combination in an ensemble learning model for air pollution forecasting. The ability of multi-objective evolutionary algorithms to find a Pareto front of solutions is used to build multiple forecast models geographically distributed in the area of interest. The proposed method has been applied for one-week NO prediction in southeastern Spain and has obtained promising results in statistical comparison with other approaches such as the union of datasets or the interpolation of the predictions for each monitoring station. The validity of the proposed spatio-temporal approach is thus demonstrated, opening up a new field in air pollution engineering.es
dc.formatapplication/pdfes
dc.format.extent19es
dc.identifier.citationFuture Generation Computer Systems 136 (2022) 15-33
dc.identifier.doihttps://doi.org/10.1016/j.future.2022.05.020
dc.identifier.issnPrint: 0167-739X
dc.identifier.issnElectronic: 1872-7115
dc.identifier.urihttp://hdl.handle.net/10201/148740
dc.languageenges
dc.publisherElsevieres
dc.relationThis work was partially funded by the SITSUS project (Ref: RTI2018-094832-B-I00), given by the Spanish Ministry of Science, Innovation and Universities (MCIU), the Spanish Agency for Research (AEI) and by the European Fund for Regional Development (FEDER) .es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0167739X22001911?via%3Dihub
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectSpatio-temporal forecastinges
dc.subjectAir pollutiones
dc.subjectMulti-objective optimizationes
dc.subjectMachine learninges
dc.subjectEnsemble learminges
dc.subject.otherCDU::0 - Generalidades.::00 - Ciencia y conocimiento. Investigación. Cultura. Humanidades.::004 - Ciencia y tecnología de los ordenadores. Informática.::004.9 - Técnicas basadas en el ordenador orientadas a aplicacioneses
dc.titleMulti-objective evolutionary spatio-temporal forecasting of air pollutiones
dc.typeinfo:eu-repo/semantics/articlees
dspace.entity.typePublicationes
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