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REGULARIZATION MODELS FOR IMPUTATION OF MISSING PRECIPITATION DATA
- Date Issued:
- 2024
- Abstract/Description:
- This study focuses on developing optimization models to estimate missing precipitation data at twenty-two sites within Kentucky State. Various optimization formulations and regularization models are explored in this context. The performance of these models is evaluated using a range of performance measures and error metrics for handling missing records. The findings revealed that regularization models performed better than optimization models. This superiority is attributed to their ability to reduce model complexity while enhancing overall performance. The study underscores the significance of regularization techniques in improving the accuracy and efficiency of precipitation data estimation.
Title: | REGULARIZATION MODELS FOR IMPUTATION OF MISSING PRECIPITATION DATA. |
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Name(s): |
Azad, Anika, author Teegavarapu, Ramesh S. V. , Thesis advisor Florida Atlantic University, Degree grantor Department of Civil, Environmental and Geomatics Engineering College of Engineering and Computer Science |
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Type of Resource: | text | |
Genre: | Electronic Thesis Or Dissertation | |
Date Created: | 2024 | |
Date Issued: | 2024 | |
Publisher: | Florida Atlantic University | |
Place of Publication: | Boca Raton, Fla. | |
Physical Form: | application/pdf | |
Extent: | 163 p. | |
Language(s): | English | |
Abstract/Description: | This study focuses on developing optimization models to estimate missing precipitation data at twenty-two sites within Kentucky State. Various optimization formulations and regularization models are explored in this context. The performance of these models is evaluated using a range of performance measures and error metrics for handling missing records. The findings revealed that regularization models performed better than optimization models. This superiority is attributed to their ability to reduce model complexity while enhancing overall performance. The study underscores the significance of regularization techniques in improving the accuracy and efficiency of precipitation data estimation. | |
Identifier: | FA00014432 (IID) | |
Degree granted: | Thesis (MS)--Florida Atlantic University, 2024. | |
Collection: | FAU Electronic Theses and Dissertations Collection | |
Note(s): | Includes bibliography. | |
Subject(s): |
Rain and rainfall Precipitation (Meteorology) Missing data (Statistics) Machine learning |
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Persistent Link to This Record: | http://purl.flvc.org/fau/fd/FA00014432 | |
Use and Reproduction: | Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
Host Institution: | FAU |