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dc.contributor.authorGonzález Mendoza, Luis Antonio 
dc.contributor.authorGonzalez-Calvo, D.
dc.contributor.authorAguilar, R.M.
dc.contributor.authorCriado-Hernández, C.
dc.contributor.otherIngeniería Química y Tecnología Farmacéutica
dc.date.accessioned2023-12-18T21:05:51Z
dc.date.available2023-12-18T21:05:51Z
dc.date.issued2021
dc.identifier.urihttp://riull.ull.es/xmlui/handle/915/34849
dc.description.abstractAnalyzing and predicting the concentration of airborne dust is vital to the economic activity and to the health of the population. In this study, we use a set of artificial neural networks that we structure through ensemble learning to yield a complex variable, such as the concentration of dust, based on actual data such as air temperature, relative humidity, atmospheric pressure and wind speed. The statistical performance indices obtained, show the effectiveness of the proposed approach through the application of a cross-validation committee. It is thus vital to have a reliable calculation method for determining relative importances that can be applied to this type of ensemble architecture by way of artificial neural networks. Unlike other relative importance methods, where calculations are done based directly on the weights in the artificial neural network and whose results in ensemble sets exhibit high dispersion, we propose our own procedure, which selectively chooses the variation in the inputs to readjust the architecture of the neural network. This allows us to measure those variables with the greatest effect on the target variable, thus obtaining the multivariate influence on the surface dust concentration through a computational model. This method thus provides a real alternative for calculating and estimating relative importance that can be generalized to any type of problem for multivariate systems modeled using artificial neural networks for both, a simple configuration, and an ensemble architectureen
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesApplied Soft Computing. 107 - 2021
dc.rightsLicencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
dc.titleMultivariate influence through neural networks ensemble: Study of Saharan dust intrusion in the Canary Islandsen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1016/j.asoc.2021.107497
dc.subject.keywordNeural networksen
dc.subject.keywordEnsemble methodsen
dc.subject.keywordRelative importanceen
dc.subject.keywordCalimaen
dc.subject.keywordSaharan dusten
dc.subject.keywordCanary Islandsen


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Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
Except where otherwise noted, this item's license is described as Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)