Convolutional attention with roll padding: Classifying PM2.5 concentration levels in the city of Beijing

Rui Gonçalves and Vitor Miguel Ribeiro

Energy, 2024, vol. 289, issue C

Abstract: A precise and timely classification of particulate matter 2.5 concentration levels is important for the design of air quality regulatory measures in a contemporaneous context characterized by the transition to a low-carbon economy. This study uses a well-known air quality dataset retrieved from the University of California at Irvine repository, which consists of a multivariate time series covering particulate matter 2.5 concentration levels in the city of Beijing for a period of 5 years. We train, test, and validate several deep learning architectures for a multinomial classification of the target variable in the period of 24 h ahead from the contemporaneous moment of action relying on historical information about the last 168 h and considering a sliding window of 24 h to construct examples. Results indicate that the internationally patented Variable Split Convolutional Attention model exhibits the best accuracy. The main novelty of this model consists of introducing bidimensional convolutional operations inside the attention block to capture the relative attention weight given to patterns of contiguous segments within different time-steps for each input variable. Therefore, a valuable deep learning architecture is presented to properly classify particulate matter 2.5 concentration levels in the atmosphere.

Keywords: Particulate matter 2.5; Deep learning; Low-carbon software technology; Convolutional layer; Multivariate time series; Attention mechanisms (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223034394
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:289:y:2024:i:c:s0360544223034394

DOI: 10.1016/j.energy.2023.130045

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().