Proceedings of the International Conference on Mathematical Sciences and Statistics 2022 (ICMSS 2022)

<Previous Article In Volume
Next Article In Volume>

Wavelet Convolutional Neural Network for Forecasting Malaysian PM10 Time Series Data

Authors
Mohd Aftar Abu Bakar1, *, Noratiqah Mohd Ariff1, Mohd Shahrul Mohd Nadzir2
1Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia
2Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia
*Corresponding author. Email: aftar@ukm.edu.my
Corresponding Author
Mohd Aftar Abu Bakar
Available Online 12 December 2022.
DOI
10.2991/978-94-6463-014-5_20How to use a DOI?
Keywords
Convolution Neural Network; Wavelet Transform; Time Series Forecasting; Air Quality
Abstract

Hourly particulate matter time series data from eight air quality monitoring stations in Peninsular Malaysia were forecast by using the Convolutional Neural Network (CNN) algorithm. Instead of using the original time series, which are time-domain sequence data, this study used the time-frequency domain sequence data retrieved by wavelet transformation. Air pollutants’ concentration considered for this study is the particulate matter with a diameter of 10 microns or less, PM10. The transformation used in this study is the Morlet wavelet transform, which is continuous wavelet transformation (CWT). Different time steps for the time series dependencies were considered to assess the PM10 dependencies on its past values. The results were compared with the results from the CNN algorithm using the original time series. It is shown that the Wavelet Convolutional Neural Network algorithm improves the forecast accuracy of the PM10 time series.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Mathematical Sciences and Statistics 2022 (ICMSS 2022)
Series
Advances in Computer Science Research
Publication Date
12 December 2022
ISBN
10.2991/978-94-6463-014-5_20
ISSN
2352-538X
DOI
10.2991/978-94-6463-014-5_20How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Mohd Aftar Abu Bakar
AU  - Noratiqah Mohd Ariff
AU  - Mohd Shahrul Mohd Nadzir
PY  - 2022
DA  - 2022/12/12
TI  - Wavelet Convolutional Neural Network for Forecasting Malaysian PM₁₀ Time Series Data
BT  - Proceedings of the International Conference on Mathematical Sciences and Statistics 2022 (ICMSS 2022)
PB  - Atlantis Press
SP  - 205
EP  - 213
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-014-5_20
DO  - 10.2991/978-94-6463-014-5_20
ID  - Bakar2022
ER  -