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An Azure ACES Early Warning System for Air Quality Index Deteriorating
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6926579/ https://www.ncbi.nlm.nih.gov/pubmed/31771273 http://dx.doi.org/10.3390/ijerph16234679 |
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author | Shih, Dong-Her Wu, Ting-Wei Liu, Wen-Xuan Shih, Po-Yuan |
author_facet | Shih, Dong-Her Wu, Ting-Wei Liu, Wen-Xuan Shih, Po-Yuan |
author_sort | Shih, Dong-Her |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6926579 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69265792019-12-24 An Azure ACES Early Warning System for Air Quality Index Deteriorating Shih, Dong-Her Wu, Ting-Wei Liu, Wen-Xuan Shih, Po-Yuan Int J Environ Res Public Health Article 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. MDPI 2019-11-24 2019-12 /pmc/articles/PMC6926579/ /pubmed/31771273 http://dx.doi.org/10.3390/ijerph16234679 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Shih, Dong-Her Wu, Ting-Wei Liu, Wen-Xuan Shih, Po-Yuan An Azure ACES Early Warning System for Air Quality Index Deteriorating |
title | An Azure ACES Early Warning System for Air Quality Index Deteriorating |
title_full | An Azure ACES Early Warning System for Air Quality Index Deteriorating |
title_fullStr | An Azure ACES Early Warning System for Air Quality Index Deteriorating |
title_full_unstemmed | An Azure ACES Early Warning System for Air Quality Index Deteriorating |
title_short | An Azure ACES Early Warning System for Air Quality Index Deteriorating |
title_sort | azure aces early warning system for air quality index deteriorating |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6926579/ https://www.ncbi.nlm.nih.gov/pubmed/31771273 http://dx.doi.org/10.3390/ijerph16234679 |
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