ResInformer: Residual Transformer-Based Artificial Time-Series Forecasting Model for PM2.5 Concentration in Three Major Chinese Cities

Mohammed A. A. Al-qaness (), Abdelghani Dahou, Ahmed A. Ewees, Laith Abualigah, Jianzhu Huai, Mohamed Abd Elaziz and Ahmed M. Helmi
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Mohammed A. A. Al-qaness: College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
Abdelghani Dahou: Mathematics and Computer Science Department, University of Ahmed DRAIA, Adrar 01000, Algeria
Ahmed A. Ewees: Department of Computer, Damietta University, Damietta 34517, Egypt
Laith Abualigah: Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan
Jianzhu Huai: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Mohamed Abd Elaziz: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Ahmed M. Helmi: Department of Computer Engineering, College of Engineering and Information Technology, Buraydah Private Colleges, Buraydah 51418, Saudi Arabia

Mathematics, 2023, vol. 11, issue 2, 1-17

Abstract: Many Chinese cities have severe air pollution due to the rapid development of the Chinese economy, urbanization, and industrialization. Particulate matter (PM2.5) is a significant component of air pollutants. It is related to cardiopulmonary and other systemic diseases because of its ability to penetrate the human respiratory system. Forecasting air PM2.5 is a critical task that helps governments and local authorities to make necessary plans and actions. Thus, in the current study, we develop a new deep learning approach to forecast the concentration of PM2.5 in three major cities in China, Beijing, Shijiazhuang, and Wuhan. The developed model is based on the Informer architecture, where the attention distillation block is improved with a residual block-inspired structure from efficient networks, and we named the model ResInformer. We use air quality index datasets that cover 98 months collected from 1 January 2014 to 17 February 2022 to train and test the model. We also test the proposed model for 20 months. The evaluation outcomes show that the ResInformer and ResInformerStack perform better than the original model and yield better forecasting results. This study’s methodology is easily adapted for similar efforts of fast computational modeling.

Keywords: air pollution; PM2.5; deep learning; time series; forecasting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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