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Air quality and urban sustainable development: the application of machine learning tools

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Air quality and urban sustainable development: the application of machine learning tools

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dc.contributor.author Molina-Gómez, N. I. es_ES
dc.contributor.author Díaz-Arévalo, J. L. es_ES
dc.contributor.author López Jiménez, Petra Amparo es_ES
dc.date.accessioned 2021-03-23T04:31:47Z
dc.date.available 2021-03-23T04:31:47Z
dc.date.issued 2021-04 es_ES
dc.identifier.issn 1735-1472 es_ES
dc.identifier.uri http://hdl.handle.net/10251/164064
dc.description.abstract [EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof International Journal of Environmental Science and Technology es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Air pollution es_ES
dc.subject Sustainability es_ES
dc.subject Forecasting es_ES
dc.subject Sustainable development goals es_ES
dc.subject Infuencing variables es_ES
dc.subject.classification TECNOLOGIA DEL MEDIO AMBIENTE es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title Air quality and urban sustainable development: the application of machine learning tools es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s13762-020-02896-6 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. International Journal of Environmental Science and Technology. 18(4):1-18. https://doi.org/10.1007/s13762-020-02896-6 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s13762-020-02896-6 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 18 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 18 es_ES
dc.description.issue 4 es_ES
dc.relation.pasarela S\418283 es_ES
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dc.subject.ods 11.- Conseguir que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles es_ES


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