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.
|Title of host publication||2015 IEEE International Conference on Big Data, IEEE Big Data 2015|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|Publication status||Published - 22 Dec 2015|
|Event||3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States|
Duration: 29 Oct 2015 → 1 Nov 2015
|Conference||3rd IEEE International Conference on Big Data, IEEE Big Data 2015|
|Period||29/10/15 → 1/11/15|
Bibliographical noteThis work is funded by NSFC 61473260, national key S&T Special Projects 2015ZX03003012-004 and Y-B2013120143 of Huawei.
- Big Data
- Ensemble Learning
- Feature Extraction
- Smog Disaster Forecasting
- Social Media