Date of Award
2024
Document Type
Thesis
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Teegavarapu, Ramesh S. V.
Abstract
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.
Extent
163 p.
Recommended Citation
Azad, Anika, "REGULARIZATION MODELS FOR IMPUTATION OF MISSING PRECIPITATION DATA" (2024). Electronic Theses and Dissertations 2020 - Present. 1481.
https://digitalcommons.fau.edu/etd_general/1481
Identifier
FA00014432
Comments
Includes bibliography.|Thesis (MS)--Florida Atlantic University, 2024.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.