Name
Abstract | ||
|---|---|---|
Mineral dust, defined as aerosol originating from the soil, can have various harmful effects to the environment and human health. The detection of dust, and particularly incoming dust storms, may help prevent some of these negative impacts. In this paper, using satellite observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation Observation (CALIPSO), we compared several machine learning algorithms to traditional physical models and evaluated their performance regarding mineral dust detection. Based on the comparison results, we proposed a hybrid algorithm to integrate physical model with the data mining model, which achieved the best accuracy result among all the methods. Further, we identified the ranking of different channels of MODIS data based on the importance of the band wavelengths in dust detection. Our model also showed the quantitative relationships between the dust and the different band wavelengths. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/eScience.2019.00012 | 2019 15th International Conference on eScience (eScience) |
Keywords | DocType | ISBN |
hybrid dust detection,data mining,physical model,satellite data,feature importance | Conference | 978-1-7281-2452-0 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peichang Shi | 1 | 0 | 0.34 |
| Qianqian Song | 2 | 0 | 0.34 |
| Janita Patwardhan | 3 | 0 | 0.34 |
| Zhibo Zhang | 4 | 6 | 2.30 |
| Jianwu Wang | 5 | 0 | 0.34 |
| Aryya Gangopadhyay | 6 | 391 | 112.49 |