A Bayesian Approach to Determining Ground Strike Points in LLS Data

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, they produce results that have the potential to be improved. This paper proposes using a Bayesian Network which is an algorithm that has not been used before to verify lightning ground strike points. The networks created for this research were learned from the data using a score- based structure learning and 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 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.