Name
Abstract | ||
|---|---|---|
It is a great challenge to offer a fine-grained and accurate PM2.5 monitoring service in urban areas as required facilities are very expensive and huge. Since PM2.5 has a significant scattering effect on visible light, large-scale user-contributed image data collected by the mobile crowdsensing bring a new opportunity for understanding the urban PM2.5. In this article, we propose a fine-grained PM2.5 estimation method based on random forest with data announced by meteorological departments and collected from smartphone users without any PM2.5 measurement devices. We design and implement a platform to collect data in the real world including the image provided by users. By combining online learning and offline learning, the method based on random forest performs well in terms of time complexity and accuracy. We compare our method with two kinds of baselines: subsets of the whole data sets and six classical models (such as logistic, naive Bayes). Six kinds of evaluation indexes (precision, recall, true-positive rate, false-positive rate, F-measure, and receiver operating characteristic curve area) are used in the evaluation. The experimental results show that our method achieves high accuracy (precision: 0.875, recall: 0.872) on PM2.5 estimation, which outperforms the other methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1177/1550147718804702 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS |
Keywords | Field | DocType |
Air quality estimation, mobile crowdsensing, semi-supervised random forest, online random forest, data fusion | Offline learning,Data mining,Data set,Naive Bayes classifier,Computer science,Baseline (configuration management),Sensor fusion,Air quality index,Time complexity,Random forest,Distributed computing | Journal |
Volume | Issue | ISSN |
14 | 10 | 1550-1477 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Feng | 1 | 0 | 1.01 |
| Ye Tian | 2 | 75 | 22.77 |
| Xiangyang Gong | 3 | 161 | 23.01 |
| Xirong Que | 4 | 142 | 15.76 |
| Wendong Wang | 5 | 821 | 72.69 |