Effective air pollution prediction by combining time series decomposition with stacking and bagging ensembles of evolving spiking neural networks

Piotr S. Maciąg, Robert Bembenik, Aleksandra Piekarzewicz, Javier Del Ser, Jesus L. Lobo, Nikola K. Kasabov

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
28 Downloads (Pure)

Abstract

In 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.
Original languageEnglish
Article number105851
JournalEnvironmental Modelling and Software
Volume170
Early online date16 Oct 2023
DOIs
Publication statusPublished online - 16 Oct 2023

Bibliographical note

Funding Information:
J. L. Lobo and J. Del Ser receive funding support from the Basque Government, Spain through grants KK-2023/00012 (BEREZ-IA) and IT1456-22 (MATHMODE) .

Publisher Copyright:
© 2023 The Author(s)

Keywords

  • Air pollution prediction
  • Evolving spiking neural networks
  • Bagging ensembles
  • Stacking ensembles
  • CEEMDAM
  • Time series decomposition

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