标题：
A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning Method
点击次数：
所属单位：
民航学院
发表刊物：
DISCRETE DYNAMICS IN NATURE AND SOCIETY
摘要：
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.
ISSN号：
1026-0226
是否译文：
否
发表时间：
2019-01-01
合写作者：
Huang, Weifang,叶博嘉,Yang, Minhao
通讯作者：
田勇
发表时间：
2019-01-01
学位：工学博士学位
职称：教授
所在单位：民航学院