Deep Learning Based Mineral Dust Detection and Feature Selection
| dc.contributor.author | Hou, Ping | |
| dc.contributor.author | Wu, Peng | |
| dc.contributor.author | Guo, Pei | |
| dc.contributor.author | Gangopadhyay, Aryya | |
| dc.date.accessioned | 2020-07-28T19:29:57Z | |
| dc.date.available | 2020-07-28T19:29:57Z | |
| dc.description.abstract | Dust storm affects human health and the environment. In this study, we develop deep learning models to identify dust from cloud and surface using MODIS observations and CALIPSO data. We also identified the best subset of channels for dust detection by a shuffling procedure and a genetic algorithm. Results show the important features determined by the two methods are very similar. And the genetic algorithm selected a subset of features that have a comparable performance with the model with all features, proving the effectiveness of the genetic algorithm. The chosen subset will reduce future data collection efforts for dust detection. | en_US |
| dc.description.sponsorship | This work is supported by the grant CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources from the National Science Foundation (grant no. OAC–1730250). The hardware in the UMBC High Performance Computing Facility (HPCF) is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS–1228778, and OAC–1726023) and the SCREMS program (grant no. DMS–0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources. | en_US |
| dc.description.uri | http://hpcf-files.umbc.edu/research/papers/CT2019Team4.pdf | en_US |
| dc.format.extent | 9 pages | en_US |
| dc.genre | technical reports | en_US |
| dc.identifier | doi:10.13016/m2r8tc-24yb | |
| dc.identifier.citation | Ping Hou et al., Deep Learning Based Mineral Dust Detection and Feature Selection, http://hpcf-files.umbc.edu/research/papers/CT2019Team4.pdf | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/19264 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | UMBC | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartofseries | HPCF–2019–14; | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | |
| dc.title | Deep Learning Based Mineral Dust Detection and Feature Selection | en_US |
| dc.type | Text | en_US |