Work

Enhancing Air Quality Prediction with Social Media and Natural Language Processing

Year: 2019

Type: conference-paper

Abstract: Accompanied by modern industrial developments, air pollution has already become a major concern for human health.Hence, air quality measures, such as the concentration of PM 2.5 , have attracted increasing attention.Even some studies apply historical measurements into air quality forecast, the changes of air quality conditions are still hard to monitor.In this paper, we propose to exploit social media and natural language processing techniques to enhance air quality prediction.Social media users are treated as social sensors with their findings and locations.After filtering noisy tweets using word selection and topic modeling, a deep learning model based on convolutional neural networks and overtweet-pooling is proposed to enhance air quality prediction.We conduct experiments on 7month real-world Twitter datasets in the five most heavily polluted states in the USA.The results show that our approach significantly improves air quality prediction over the baseline that does not use social media by 6.9% to 17.7% in macro-F1 scores.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/p19-1251

Authors: Jyun-Yu Jiang, Xue Sun, Wei Wang, Sean Young

Institution: University of California, Los Angeles

Language: English

Topic: Air Quality Monitoring and Forecasting

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Funders: National Science Foundation, National Institutes of Health