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A time series forecasting based multi-criteria methodology for air quality prediction

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dc.contributor.authorEspinosa Fernández, Raquel
dc.contributor.authorPalma Méndez, José Tomás
dc.contributor.authorJiménez Barrionuevo, Fernando
dc.contributor.authorKamińska, Joanna
dc.contributor.authorSciavicco, Guido
dc.contributor.authorLucena Sánchez, Estrella
dc.contributor.departmentIngeniería de la Información y las Comunicaciones
dc.date.accessioned2025-01-18T19:08:12Z
dc.date.available2025-01-18T19:08:12Z
dc.date.issued2021-09-07
dc.description.abstractThere is a very extensive literature on the design and test of models of environmental pollution, especially in the atmosphere. Current and recent models, however, are focused on explaining the causes and their temporal relationships, but do not explore, in full detail, the performances of pure forecasting models. We consider here three years of data that contain hourly nitrogen oxides concentrations in the air; exposure to high concentrations of these pollutants has been indicated as potential cause of numerous respiratory, circulatory, and even nervous diseases. Nitrogen oxides concentrations are paired with meteorological and vehicle traffic data for each measure. We propose a methodology based on exactness and robustness criteria to compare different pollutant forecasting models and their characteristics. 1DCNN, GRU and LSTM deep learning models, along with Random Forest, Lasso Regression and Support Vector Machines regression models, are analyzed with different window sizes. As a result, our best models offer a 24-hours ahead, very reliable prediction of the concentration of pollutants in the air in the considered area, which can be used to plan, and implement, different kinds of interventions and measures to mitigate the effects on the population.es
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dc.format.extent25es
dc.identifier.citationApplied Soft Computing 113 (2021) 107850
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2021.107850
dc.identifier.issnPrint: 1568-4946
dc.identifier.issnElectronic: 1872-9681
dc.identifier.urihttp://hdl.handle.net/10201/148742
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). This work was supported by the Science and Technology Agency, Séneca Foundation, Comunidad Autónoma Región de Murcia , Spain through the research projects 00004/COVI/20 and 00007/COVI/20.es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1568494621007729?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.subjectAir qualityes
dc.subjectMultivariate time series forecastinges
dc.subjectDeep learninges
dc.subjectMulti-criteria decision support systemses
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.titleA time series forecasting based multi-criteria methodology for air quality predictiones
dc.typeinfo:eu-repo/semantics/articlees
dspace.entity.typePublicationes
relation.isAuthorOfPublication490d158d-e40d-4fdf-99f0-912bd4aa5a4e
relation.isAuthorOfPublication47706328-5460-433a-b86f-dcbcc9841c53
relation.isAuthorOfPublication.latestForDiscovery490d158d-e40d-4fdf-99f0-912bd4aa5a4e
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