Application of Deep Learning for Air Quality Predictions, Remote Sensing Processing and Long-term Climate Index Forecasting
| dc.contributor.advisor | Choi, Yunsoo | |
| dc.contributor.committeeMember | Jiang, Xun | |
| dc.contributor.committeeMember | Wang, Yuxuan | |
| dc.contributor.committeeMember | Eick, Christoph F. | |
| dc.creator | Lops, Yannic | |
| dc.creator.orcid | 0000-0002-2594-7845 | |
| dc.date.accessioned | 2022-06-17T21:45:04Z | |
| dc.date.created | December 2021 | |
| dc.date.issued | 2021-12 | |
| dc.date.submitted | December 2021 | |
| dc.date.updated | 2022-06-17T21:45:05Z | |
| dc.description.abstract | The advancement and development of new technology provide atmospheric scientists and modelers to acquire an overwhelming amount of data on meteorology and air quality from space, numerical simulations, and in-situ monitoring sites. Integrating these data sources provides unique opportunities to enhance understanding of atmospheric processes to better simulate and forecast these processes. While Global Climate Models and Chemical Transport Models have undergone significant optimizations and improvements over the past decades, they are still unable to provide fully reliable biogenic air quality predictions or long-term climate forecasting. These limitations can be alleviated and addressed by incorporating in-situ measurements and remote sensing products into data assimilation or reanalysis techniques. While ground-based remote sensing measurements provide detailed point observations, they lack the spatial coverage of remote sensing-derived measurements. Unfortunately, these remote sensing measurements experience issues caused by outside factors such as cloud cover contamination and false reflectance. Internal issues involve sensor errors that corrupt or lead to failed measurements of the data. This study utilizes the advanced capability of several deep learning models for the forecasting of pollen concentrations by up to 7 days; the imputation of remote sensing measurements spatially with partial convolutional neural networks and subsequent revision to incorporate spatio-temporal imputation; and long-term forecasting system of the climate index Nino3.4 by up to 36 months. | |
| dc.description.department | Earth and Atmospheric Sciences, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Lops, Y., Choi, Y., Eslami, E. et al. Real-time 7-day forecast of pollen counts using a deep convolutional neural network. Neural Comput & Applic 32, 11827–11836 (2020). https://doi.org/10.1007/s00521-019-04665-0; and in: Lops, Y., Pouyaei, A., Choi, Y., Jung, J., Salman, A. K., & Sayeed, A. (2021). Application of a partial convolutional neural network for estimating geostationary aerosol optical depth data. Geophysical Research Letters, 48, e2021GL093096. https://doi.org/10.1029/2021GL093096 | |
| dc.identifier.uri | https://hdl.handle.net/10657/9265 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Deep Learning | |
| dc.subject | Air Quality | |
| dc.subject | Climate | |
| dc.subject | Forecast | |
| dc.subject | Remote Sensing | |
| dc.subject | Convolution Neural Network | |
| dc.subject | Aerosols | |
| dc.subject | Imputation | |
| dc.title | Application of Deep Learning for Air Quality Predictions, Remote Sensing Processing and Long-term Climate Index Forecasting | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| dcterms.accessRights | The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period. | |
| local.embargo.lift | 2023-12-01 | |
| local.embargo.terms | 2023-12-01 | |
| thesis.degree.college | College of Natural Sciences and Mathematics | |
| thesis.degree.department | Earth and Atmospheric Sciences, Department of | |
| thesis.degree.discipline | Atmospheric Sciences | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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