Variational Bayesian Network with Information Interpretability Filtering for Air Quality Forecasting

Xue-Bo Jin, Zhong-Yao Wang, Wen-Tao Gong, Jian-Lei Kong (), Yu-Ting Bai, Ting-Li Su, Hui-Jun Ma and Prasun Chakrabarti
Additional contact information
Xue-Bo Jin: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Zhong-Yao Wang: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Wen-Tao Gong: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Jian-Lei Kong: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Yu-Ting Bai: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Ting-Li Su: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Hui-Jun Ma: Artificial Intelligence College, Beijing Technology and Business University, Beijing 100048, China
Prasun Chakrabarti: Department of Computer Science and Engineering, ITM SLS Baroda University, Vadodara 391510, India

Mathematics, 2023, vol. 11, issue 4, 1-18

Abstract: Air quality plays a vital role in people’s health, and air quality forecasting can assist in decision making for government planning and sustainable development. In contrast, it is challenging to multi-step forecast accurately due to its complex and nonlinear caused by both temporal and spatial dimensions. Deep models, with their ability to model strong nonlinearities, have become the primary methods for air quality forecasting. However, because of the lack of mechanism-based analysis, uninterpretability forecasting makes decisions risky, especially when the government makes decisions. This paper proposes an interpretable variational Bayesian deep learning model with information self-screening for PM2.5 forecasting. Firstly, based on factors related to PM2.5 concentration, e.g., temperature, humidity, wind speed, spatial distribution, etc., an interpretable multivariate data screening structure for PM2.5 forecasting was established to catch as much helpful information as possible. Secondly, the self-screening layer was implanted in the deep learning network to optimize the selection of input variables. Further, following implantation of the screening layer, a variational Bayesian gated recurrent unit (GRU) network was constructed to overcome the complex distribution of PM2.5 and achieve accurate multi-step forecasting. The high accuracy of the proposed method is verified by PM2.5 data in Beijing, China, which provides an effective way, with multiple factors for PM2.5 forecasting determined using deep learning technology.

Keywords: multiple factors; time series forecasting; deep learning; interpretability; data filtering; variational Bayesian (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/4/837/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/4/837/ (text/html)

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:gam:jmathe:v:11:y:2023:i:4:p:837-:d:1060152

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().