Identification of Lightning Strikes to Towers Using Machine-Learning Approach

Lightning often strikes tall objects and, hence, poses significant threat to infrastructure. Modern lightning locating systems can provide crucial information about lightning, such as strike coordinates, peak current, etc., but cannot tell whether the stroke terminated on a tall object such as tower. Lightning electromagnetic field pulses (LEMP) have been used to infer lightning currents. Using the unique electric field pulse signatures of the tower terminated lightning collected at the Lightning Observatory in Gainesville (LOG), Florida, we present a machine-learning approach to classify lightning strikes to towers. The classification model used in this study is based on supervised Multi-Layer Perceptron model (MLP), aiming to capture complex pulse patterns with the neural network architecture. We refined the model by tuning the configuration of the training dataset, and validated its performance with Local Interpretable Model-agnostic Explanations. The result shows that an overall 99.97% classification accuracy can be achieved, with 90% and 99.97% classification accuracy for tower terminated lightning and non-tower terminated lightning, respectively.