Date of Award
3-2023
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Operational Sciences
First Advisor
Nathan B. Gaw, PhD
Abstract
The uncertainty of lightning constantly threatens many weather-sensitive fields where the slightest presence of lightning can endanger valuable personnel and assets. The consequences of delaying operations have incited the research of methods that can accurately predict the location of future lightning strikes from the current weather conditions. High-dimensional remote sensing modalities contain information capable of detecting significant patterns and intensities within storms that could indicate the presence of lightning. This thesis induces sparsity into convolutional neural networks (CNNs) and remote sensing modalities through a combination of regularization and tensor decomposition techniques to call attention to sparse features that are most indicative of lightning activity. The developed models produce accurate predictions of the general pattern of true lightning strikes at lower time lags. The results demonstrate the potential of using CNNs in combination with sparse methods that focus on important features for the prediction of close-range lightning activity.
AFIT Designator
AFIT-ENS-MS-23-M-146
Recommended Citation
Metzgar, Grace E., "Inducing Sparsity Within High-dimensional Remote Sensing Modalities for Lightning Prediction" (2023). Theses and Dissertations. 7008.
https://scholar.afit.edu/etd/7008
Comments
A 12-month embargo was observed.
Approved for public release. Case number on file.