Transfer learning with weather radar
| dc.contributor.author | Gooch, S. Ryan, author | |
| dc.contributor.author | Chandrasekar, V., advisor | |
| dc.contributor.author | Cheney, Margaret, committee member | |
| dc.contributor.author | Chavéz, José, committee member | |
| dc.contributor.author | Suryanarayanan, Sid, committee member | |
| dc.date.accessioned | 2020-06-22T11:53:45Z | |
| dc.date.available | 2022-06-15T11:53:45Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | This work presents the culmination of the doctoral research by the author in exploring modern methods of Data Discovery in weather radar data, improvements in the cyberinfrastructure concerning multi-dimensional gridded data, with a concentration on real-time data streaming, and experimental use cases involving real world datasets. Included in this work is a successful method for the classification of weather radar image data using convolutional neural networks, with inspiration drawn from the subfield of Transfer Learning in the Computer Vision community. Once this model was developed, it was deployed on single radar data from each of the radars in the CASA DFW network to assign labels to support a human-in-the-loop semi-supervised method for data discovery in the weather radar scans. This model has been furthermore applied to the WSR-88D network of dual-polarimetric weather radars in the United States to demonstrate the model's generalizability, and its utility in discovering phenomena of interest in vast datasets. This work discusses the end-to-end development of the data discovery system, with special focus on initial data labeling, choices and tradeos in model architecture, and training concerns in the machine learning model. This represents the rst published research known to the authors on utilizing the power of transfer learning to transfer the learning of high quality convolutional neural networks trained on photographic images to the weather radar image domain. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Gooch_colostate_0053A_15943.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/208552 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | deep learning | |
| dc.subject | radar | |
| dc.subject | big data | |
| dc.subject | weather | |
| dc.subject | machine learning | |
| dc.title | Transfer learning with weather radar | |
| dc.type | Text | |
| dcterms.embargo.expires | 2022-06-15 | |
| dcterms.embargo.terms | 2022-06-15 | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Electrical and Computer Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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