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2024 | OriginalPaper | Chapter

Application of Discrete Wavelet Transform and Tree-Based Ensemble Machine Learning for Modeling of Particulate Matter Concentrations

Authors : Maya Stoimenova-Minova, Snezhana Gocheva-Ilieva, Atanas Ivanov

Published in: Mathematical Methods for Engineering Applications

Publisher: Springer Nature Switzerland

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Abstract

The study of air pollution is an extremely important and urgent problem to be solved on a global and local scale. In this field, huge arrays of measurement data are accumulating, for the analysis of which various approaches based on mathematical, statistical, and machine learning (ML) methods are developed. In this paper, we investigate the application of different discrete wavelet transforms (DWT) families, coupled with state-of-the-art ML algorithms to predict concentrations of particulate matter PM10. Average daily data for this pollutant and several meteorological time series for a period of 630 days were used. A hybrid type models with wavelet decomposition of the initial time series and the application of predictive ensembles (Arcing, Arc-x4) were obtained. All models are cross-validated. The models are applied for short-term pollution forecasts.

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Metadata
Title
Application of Discrete Wavelet Transform and Tree-Based Ensemble Machine Learning for Modeling of Particulate Matter Concentrations
Authors
Maya Stoimenova-Minova
Snezhana Gocheva-Ilieva
Atanas Ivanov
Copyright Year
2024
Book
Mathematical Methods for Engineering Applications

Print ISBN: 978-3-031-49217-4

Electronic ISBN: 978-3-031-49218-1

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-49218-1_12

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