Modelling the Odds of Occurrence of Positive Narrow Bipolar Pulses Based on the Wave Characteristics

Thabrew, Kaludura Anupama Seuwandi; Vayanganie, A.; Gunasekara, Lasitha; Abeywardhana, R.; Fernando, Mahendra

doi:10.1109/iclpandsipda54065.2021.9627484

Cited by 1 publication

(3 citation statements)

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“…In contrast, PD and FWHM were insignificant in both the BLR and RF models, making them less useful in identifying the type of NBP. These results are in agreement with those of a previous study conducted in the tropics [3].…”

Section: Resultssupporting

confidence: 94%

“…CIDs are associated with strong changes in E-field radio frequency radiation. Due to their short duration (10-30 µs) and distinctive initial and overshoot pulse, they are referred to as Narrow Bipolar Pulses (NBPs) [3]. Depending on the polarity of the NBP's initial pulse, they are classified into two categories: Positive NBP (PNBP) and Negative NBP (NNBP) [4] as shown in Figures 1 and 2.…”

Section: Introductionmentioning

confidence: 99%

“…Few studies have been conducted to study the statistical characteristics of NBPs. Studies by Gunasekara et al [5], Sharma et al [6], and Thabrew et al [3] have examined the characteristics of NBPs and their associations with the type of NBP. The results of these studies suggest a significant difference between the types of NBP with respect to each characteristic.…”

Section: Introductionmentioning

confidence: 99%

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

Thabrew,

Vayanganie,

Fernando

et al. 2024

J. Phys.: Conf. Ser.

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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.

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Section: Resultssupporting

confidence: 94%

Section: Introductionmentioning

confidence: 99%