Date of Award
3-22-2018
Document Type
Thesis
Degree Name
Master of Science in Applied Mathematics
Department
Department of Mathematics and Statistics
First Advisor
Richard Seymour, PhD.
Abstract
Electric Field Mills (EFMs) located in the region surrounding Cape Canaveral record the electrification of the atmosphere near them. Research studying how these sensors could improve lightning warnings has had mixed results. This paper used a Convolutional Recurrent Neural Network (CRNN) and data from 30 EFMs from May-July of 2012-2016. The mean was calculated for every 60 second period and 30 minutes of this summarized data was used to create a lightning prediction with a warning period of 15 minutes. This method achieved a True Positive Rate (TPR) of 77.6%, a False Positive Rate (FPR) of 8.3%, a False Discovery Rate (FDR) of 48.1%, and an Operational Utility Index (OUI) of 53.9% (Kehrer et al., 2006). This suggests that the EFM sensor array, when used as a means to measure the electrification of the entire region, is capable of effectively predicting lightning for a 5-mile radius near Cape Canaveral. Moreover, achieving a 53.9% on the OUI rivals the best methods currently used implying that incorporating EFMs into lightning forecasting may reduce the FPR and save millions of dollars in delay and cancellation costs.
AFIT Designator
AFIT-ENC-MS-18-M-002
DTIC Accession Number
AD1055963
Recommended Citation
Hill, Daniel E., "Lightning Prediction Using Artificial Neural Networks and Electric Field Mill Data" (2018). Theses and Dissertations. 1737.
https://scholar.afit.edu/etd/1737