Effective Air Pollution Prediction by Combining Time Series Decomposition with Stacking and Bagging Ensembles of Evolving Spiking Neural Networks

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aut.relation.articlenumber105851
aut.relation.endpage105851
aut.relation.journalEnvironmental Modelling and Software
aut.relation.startpage105851
aut.relation.volume170
dc.contributor.authorMaciąg, Piotr S
dc.contributor.authorBembenik, Robert
dc.contributor.authorPiekarzewicz, Aleksandra
dc.contributor.authorDel Ser, Javier
dc.contributor.authorLobo, Jesus L
dc.contributor.authorKasabov, Nikola K
dc.date.accessioned2023-11-09T04:14:42Z
dc.date.available2023-11-09T04:14:42Z
dc.date.issued2023-10-28
dc.description.abstractIn this article, we introduce a new approach to air pollution prediction using the CEEMDAN time series decomposition method combined with the two-layered ensemble of predictors created based on the stacking and bagging techniques. The proposed ensemble approach is outperforming other selected state-of-the-art models when the bagging ensemble consisting of evolving Spiking Neural Networks (eSNNs) is used in the second layer of the stacking ensemble. In our experiments, we used the PM10 air pollution and weather dataset for Warsaw. As the results of the experiments show, the proposed ensemble can achieve the following error and agreement values over the tested dataset: error RMSE 6.91, MAE 5.14 and MAPE 21%; agreement IA 0.94. In addition, this article provides the computational and space complexity analysis of eSNNs predictors and offers a new encoding method for spiking neural networks that can be effectively applied for values of skewed distributions.
dc.identifier.citationEnvironmental Modelling and Software, ISSN: 1364-8152 (Print); 1364-8152 (Online), Elsevier, 170, 105851-105851. doi: 10.1016/j.envsoft.2023.105851
dc.identifier.doi10.1016/j.envsoft.2023.105851
dc.identifier.issn1364-8152
dc.identifier.issn1364-8152
dc.identifier.urihttp://hdl.handle.net/10292/16909
dc.languageen
dc.publisherElsevier
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S1364815223002372
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject37 Earth Sciences
dc.subject46 Information and Computing Sciences
dc.subject3701 Atmospheric Sciences
dc.subject4611 Machine Learning
dc.subjectEnvironmental Engineering
dc.titleEffective Air Pollution Prediction by Combining Time Series Decomposition with Stacking and Bagging Ensembles of Evolving Spiking Neural Networks
dc.typeJournal Article
pubs.elements-id528160
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