Predicting the Type of Narrow Bipolar Pulse: A Machine Learning Approach

Abstract

Narrow Bipolar Pulses (NBP) depicts the electric field (E-Field) changes due to a Compact Intra-cloud Discharge (CID) and are of two types namely Positive NBP (PNBP) and Negative NBP (NNBP). In this study, 437 NBPs were statistically investigated using a dataset collected in Sri Lanka in 2013, 2015, 2016 and 2017. Seven independent variables (Pulse Duration (PD), Rise Time (RT), Slow Front Duration (SFD), Zero Crossing Time (ZCT), Full Width at Half Maximum (FWHM) and Ratio between the Initial and Overshoot Peak Amplitudes (RIOPA)) and one dependent variable (Type of NBP) were analyzed. Two machine learning classification models, the Random Forest (RF) model and the Binary Logistic Regression (BLR) model, were used to predict the dependent variable based on the independent variables. Two models were compared in terms accuracy (ACC), sensitivity (SE), specificity (SP), AUC (Area Under the Curve)-ROC (Receiver Operator Characteristic) curve and kappa statistic. RF model scored the highest in terms of ACC (0.91), specificity (0.94), Kappa statistics (0.82) and AUC (0.98). In conclusion, RF model had the best performance in predicting the type of NBP hence can be used as a suitable automated method to classify the type of NBP.