Smog disaster forecasting using social web data and physical sensor data

Jiaoyan Chen, Huajun Chen, Daning Hu, J. Z. Pan, Yalin Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Smog disaster is a type of air pollution event that negatively affects people's life and health. Forecasting smog disasters may largely reduce potential loss that they may cause. However, it is a great challenge since smog disasters are often caused by many complex factors. With the availability of huge amounts of data from the social web and physical sensors, covering information of air quality, meteorology, social event, human mobility, people's opinion, etc., it becomes possible to utilize such big data to forecast smog disasters. Especially, we can investigate the effect of social activities in smog disaster forecasting with the help of social web, which is ignored in traditional studies. In this paper, we propose a big data approach named B-Smog for smog disaster forecasting. It mainly has two components: 1) features extraction from multiple data sources to model the factors that indicate the appearance or disappearance of a smog disaster like traffic condition, human mobility, weather condition and air pollution transportation; 2) learning and predicting with heterogeneous features in multiple views. For the second component, we propose a prediction model based on an ensemble learning framework and artificial neural networks (ANNs), which achieves high accuracy in this application and can also be applied to other similar problems. We present the effectiveness of B-Smog through two cases studies in Beijing and Shanghai, and evaluate the accuracy of the prediction model through comparing it with some baselines. Moreover, the empirical findings of our study can also support decision making in smog disaster management
Original languageEnglish
Title of host publication2015 IEEE International Conference on Big Data (Big Data)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)978-1-4799-9926-2
ISBN (Print)978-1-4799-9925-5
Publication statusPublished - 28 Dec 2015
Event2015 IEEE International Conference on Big Data - Santa Clara, United States
Duration: 29 Oct 20151 Nov 2015


Conference2015 IEEE International Conference on Big Data
Abbreviated titleBigData 2015
Country/TerritoryUnited States
CitySanta Clara
Internet address

Keywords / Materials (for Non-textual outputs)

  • Smog Disaster Forecasting
  • Big Data
  • Social Media
  • Ensemble Learning
  • Feature Extraction


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