Name
Abstract | ||
|---|---|---|
The first step to deal with the significant issue of air pollution in China and elsewhere in the world is to monitor it. While more physical monitoring stations are built, current coverage is limited to large cities with most other places under-monitored. In this paper we propose a complementary approach to monitor Air Quality Index (AQI): using machine learning models to estimate AQI from social media posts. We propose a series of progressively more sophisticated machine learning models, culminating in a Markov Random Field model that utilizes the text content in social media as well as the spatiotemporal correlation among cities and days. Our extensive experiments on Sina Weibo data from 108 cities during a one-month period demonstrate the accurate AQI prediction performance of our approach. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ASONAM.2014.6921638 | ASONAM |
Keywords | Field | DocType |
text content,aqi prediction performance,machine learning models,markov random field model,learning (artificial intelligence),air pollution,china,environmental science computing,air quality index,social media posts,sina weibo data,social networking (online),text analysis,markov processes,spatiotemporal correlation,air quality,pollution,atmospheric modeling,correlation | Data science,Data mining,Observational learning,Markov random field,Computer science,Air quality index,Air pollution,Artificial intelligence,Decision-making models,Social learning theory,Social media,Information cascade,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4799-5876-4 | 13 | 0.82 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shike Mei | 1 | 78 | 3.50 |
| Han Li | 2 | 13 | 1.16 |
| jing fan | 3 | 264 | 46.24 |
| Xiaojin Zhu | 4 | 3586 | 222.74 |
| Charles R. Dyer | 5 | 13 | 0.82 |