气候变化领域集成服务门户(CCPortal): A long short-term memory approach to predicting air quality based on social media data

CCPortal

DOI	10.1016/j.atmosenv.2020.117411
	A long short-term memory approach to predicting air quality based on social media data
	Zhai W.; Cheng C.
发表日期	2020
ISSN	13522310
卷号	237
英文摘要	Air pollution, such as PM2.5 (particulate matter with an aerodynamic equivalent diameter of less than 2.5 μm), PM10 (particulate matter with an aerodynamic equivalent diameter of less than 10 μm), NOx, and SOx, is a global concern because it may cause many chronic and fatal diseases, especially in developing countries. To better address air pollution problems, an important step is the timely and accurate prediction of air quality. Traditional methods are mainly based on meteorological data, regression model data, remote sensing data and different retrieval methods. Numerous studies on deep learning methods have suggested that these approaches may be able to perform accurate predictions for complex systems. In this paper, a long short-term memory (LSTM) approach for predicting air quality is proposed; moreover, meteorological data are used and Chinese social media is investigated as a proxy for public perceptions and responses for air quality prediction. We gathered daily air quality data, meteorological data and Weibo check-in data for Beijing, China from January 1, 2015 to December 31, 2016. The average sentiment of the related Weibo posts was selected as the public response proxy. The performance of our proposed model is evaluated based on real data. The root-mean-square error (RMSE) and the mean absolute error (MAE) indicated that our method presented better prediction results than traditional methods in terms of the PM2.5, PM10, O3, NO2, SO2 and CO concentrations. We focused on the prediction performance during the 2015 China Victory Day Parade period, during which social and political factors played an important role in air quality predictions. The results indicated that the proposed method, which incorporates public response data, was especially suitable for predicting the air quality in extreme short-term social events and provides a timely social measurement and feedback for environmental problems. © 2020 Elsevier Ltd
英文关键词	2015 China Victory day parade; Air quality prediction; LSTM; Social media
语种	英语
scopus关键词	Aerodynamics; Air quality; Brain; Deep learning; Developing countries; Forecasting; Learning systems; Logistic regression; Mean square error; Meteorology; Nitrogen oxides; Particles (particulate matter); Remote sensing; Social networking (online); Accurate prediction; Aerodynamic equivalent diameters; Air quality prediction; Environmental problems; Meteorological data; Prediction performance; Remote sensing data; Root mean square errors; Long short-term memory; carbon monoxide; nitric oxide; ozone; sulfur oxide; aerodynamics; air quality; atmospheric pollution; developing world; machine learning; memory; particle size; particulate matter; prediction; social media; air pollutant; air pollution; air quality; Article; deep learning; humidity; long short term memory network; meteorology; particulate matter; politics; precipitation; pressure; priority journal; remote sensing; short term memory; social media; temperature; wind speed; Beijing [China]; China
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/145049
作者单位	College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China; College of Engineering, Peking University, Peking University, Beijing, 100871, China
推荐引用方式 GB/T 7714	Zhai W.,Cheng C.. A long short-term memory approach to predicting air quality based on social media data[J],2020,237.
APA	Zhai W.,&Cheng C..(2020).A long short-term memory approach to predicting air quality based on social media data.Atmospheric Environment,237.
MLA	Zhai W.,et al."A long short-term memory approach to predicting air quality based on social media data".Atmospheric Environment 237(2020).