A Spatiotemporal Recurrent Neural Network for Prediction of Atmospheric PM2.5
A Case Study of Beijing
Bibliographic Data
| ID | 22108997 |
|---|---|
| Authors | Bo Liu (0000-0002-6536-517X, University of Auckland), Shuo Yan (0000-0001-7744-3588, Chinese Academy of Sciences), Jianqiang Li (0000-0002-0029-1747, Beijing University of Technology), Yong Li (0000-0002-4884-9930, Beijing University of Technology), Jianlei Lang (0000-0001-5575-0018, Beijing University of Technology), Guangzhi Qu (0000-0003-4047-9514, Oakland University) |
| Year | 2021 |
| Volume | 8 |
| Issue | 3 |
| Pages | 578-588 |
| Publication date | 2021-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2021.3056410 |
| OpenAlex | W3132807016 |
| Language | EN |
| Citations received | 4 |
| References cited | 33 |
With rapid industrial development, air pollution problems, especially in urban and metropolitan centers, have become a serious societal problem and require our immediate attention and comprehensive solutions to protect human and animal health and the environment. Because bad air quality brings prominent effects on our daily life, how to forecast future air quality accurately and tenuously has emerged as a priority for guaranteeing the quality of human life in many urban areas worldwide. Existing models usually neglect the influence of wind and do not consider both distance and similarity to select the most related stations, which can provide significant information in prediction. Therefore, we propose a Geographic Self-Organizing Map (GeoSOM) spatiotemporal gated recurrent unit (GRU) model, which clusters all the monitor stations into several clusters by geographical coordinates and time-series features. For each cluster, we build a GRU model and weighted different models with the Gaussian vector weights to predict the target sequence. The experimental results on real air quality data in Beijing validate the superiority of the proposed method over a number of state-of-the-art ones in metrics, such as ${R} ^{2}$ , mean relative error (MRE), and mean absolute error (MAE). The MAE, MRE, and ${R} ^{2}$ are 16.1, 0.79, and 0.35 at the Gucheng station and 19.53, 0.82, and 0.36 at the Dongsi station
Air quality index · Algorithm · Artificial neural network · Beijing · Data mining · Geography · Meteorology · Notation · Air Quality and Health Impacts · Air Quality Monitoring and Forecasting · Atmospheric chemistry and aerosols · Computer Science · Mathematics · Artificial Intelligence
| Citations per year | 1.33 |
|---|---|
| Citation span | 2023 - 2025 (3) |
| Highly cited | No |