A Bayesian approach to lightning ground-strike points analysis
Date
2022
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Abstract
Studying cloud-to-ground lightning strokes and ground strike points provides an alternative method of lightning mapping for lightning risk assessment. Various k-means algorithms have been used to verify the ground strike points from lightning locating systems. These algorithms produce results but have the potential to be improved.
This research report proposes using Bayesian Network which is a model that has not been used before to verify lightning ground strike points. A Bayesian Network is a probabilistic graphical model that makes use of Bayes Theorem to represent the conditional dependencies of variables. The network created for this research were learned from the data using a score-based structure learning and the Bayesian Information Criterion score function was used. The models were evaluated using a confusion matrix and a kappa index. They produced accuracies ranging from 86% to 94% with a kappa index of up to 0.76.
The results from the Bayesian Network models are within the range of the available algorithms used currently to analyse lightning ground strike points but have an advantage of not needing a predetermined distance, easy to interpret and as well as being suitable for small data sets. The use of a Bayesian network is a good candidate for an alternative method to analyse lightning ground strike points.
Description
A Research Report submitted in partial fulfilment of the requirements for the degree of Master of Science to the Faculty of Science, University of the Witwatersrand, Johannesburg
Keywords
Bayesian Approach, Lightning Ground-Strike, Cloud-to-ground lightning