An Azure Aces Early Warning System for Air Quality Index Deteriorating
Bibliographic Data
| ID | 15488152 |
|---|---|
| Authors | Dong‐Her Shih (0000-0003-1605-8488, National Yunlin University of Science and Technology, corresponding author), Ting-Wei Wu (National Yunlin University of Science and Technology), Wenxuan Liu (0000-0003-1227-0991, National Yunlin University of Science and Technology), Wen-Xuan Liu (Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliu 640, Taiwan), Po-Yuan Shih (National Yunlin University of Science and Technology) |
| Year | 2019 |
| Volume | 16 |
| Issue | 23 |
| Pages | 4679-4679 |
| Publication date | 2019-11-24 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph16234679 |
| PMID | 31771273 |
| OpenAlex | W2989623338 |
| Language | EN |
| Citations received | 1 |
| References cited | 33 |
With the development of industrialization and urbanization, air pollution in many countries has become more serious and has affected people's health. The air quality has been continuously concerned by environmental managers and the public. Therefore, accurate air quality deterioration warning system can avoid health hazards. In this study, an air quality index (AQI) warning system based on Azure cloud computing platform is proposed. The prediction model is based on DFR (Decision Forest Regression), NNR (Neural Network Regression), and LR (Linear Regression) machine learning algorithms. The best algorithm was selected to calculate the 6 pollutants required for the AQI calculation of the air quality monitoring in real time. The experimental results show that the LR algorithm has the best performance, and the method of this study has a good prediction on the AQI index warning for the next one to three hours. Based on the ACES system proposed, it is hoped that it can prevent personal health hazards and help to reduce medical costs in public
Air pollution · Air quality index · Artificial neural network · Environmental health · Geography · Index (typography · Industrialisation · Machine learning · Meteorology · Public health · Quality (philosophy · Regression analysis · Telecommunications · Urbanization · Warning system · Air Quality and Health Impacts · Air Quality Monitoring and Forecasting · Computer Science · COVID-19 impact on air quality · Environmental Science · Medicine
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,17 |
| Citation span | 2020 - 2020 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |