A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning Method
In order to reduce the air pollution impacts by aircraft operations around airports, a fast and accurate prediction of air quality related to aircraft operations is an essential prerequisite. This article proposes a new framework with a combination of the standard assessment procedure and machine le...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2019/1562537 |
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| _version_ | 1832548950189539328 |
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| author | Yong Tian Weifang Huang Bojia Ye Minhao Yang |
| author_facet | Yong Tian Weifang Huang Bojia Ye Minhao Yang |
| author_sort | Yong Tian |
| collection | DOAJ |
| description | In order to reduce the air pollution impacts by aircraft operations around airports, a fast and accurate prediction of air quality related to aircraft operations is an essential prerequisite. This article proposes a new framework with a combination of the standard assessment procedure and machine learning methods for fast and accurate prediction of air quality in airports. Instead of taking some specific pollutant as concerned metric, we introduce the air quality index (AQI) for the first time to evaluate the air quality in airports. Then, following the standard assessment procedure proposed by International Civil Aviation Organization (ICAO), the airports AQIs in different scenarios are classified with consideration of the airport configuration, actual flight operations, aircraft performance, and related meteorological data. Taking the AQI classification results as sample data, several popular supervised learning methods are investigated for accurately predicting air quality in airports. The numerical tests implicate that the accuracy rate of prediction could reach more than 95% with only 0.022 sec; the proposed framework and the results could be used as the foundation for improving air quality impacts around airports. |
| format | Article |
| id | doaj-art-5a5b75c903f24ec0820251d97670d8e5 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-5a5b75c903f24ec0820251d97670d8e52025-02-03T06:12:35ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2019-01-01201910.1155/2019/15625371562537A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning MethodYong Tian0Weifang Huang1Bojia Ye2Minhao Yang3College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaIn order to reduce the air pollution impacts by aircraft operations around airports, a fast and accurate prediction of air quality related to aircraft operations is an essential prerequisite. This article proposes a new framework with a combination of the standard assessment procedure and machine learning methods for fast and accurate prediction of air quality in airports. Instead of taking some specific pollutant as concerned metric, we introduce the air quality index (AQI) for the first time to evaluate the air quality in airports. Then, following the standard assessment procedure proposed by International Civil Aviation Organization (ICAO), the airports AQIs in different scenarios are classified with consideration of the airport configuration, actual flight operations, aircraft performance, and related meteorological data. Taking the AQI classification results as sample data, several popular supervised learning methods are investigated for accurately predicting air quality in airports. The numerical tests implicate that the accuracy rate of prediction could reach more than 95% with only 0.022 sec; the proposed framework and the results could be used as the foundation for improving air quality impacts around airports.http://dx.doi.org/10.1155/2019/1562537 |
| spellingShingle | Yong Tian Weifang Huang Bojia Ye Minhao Yang A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning Method Discrete Dynamics in Nature and Society |
| title | A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning Method |
| title_full | A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning Method |
| title_fullStr | A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning Method |
| title_full_unstemmed | A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning Method |
| title_short | A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning Method |
| title_sort | new air quality prediction framework for airports developed with a hybrid supervised learning method |
| url | http://dx.doi.org/10.1155/2019/1562537 |
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