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Analyzing Lightning Strike Susceptibility Using the Elliptical Fitting Method with a Principal Component Analysis Approach Lovytaji, Helmalia A.; Rozikan, Rozikan; Kuncoro, Djati C.; Karisma, Ria Dhea Layla Nur
Jurnal Varian Vol 8 No 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3183

Abstract

Lightning is a high-current discharge that occurs in Cumulonimbus clouds, with CG (Cloud to Ground)lightning strikes posing significant dangers, especially to human life. Pasuruan, located in the highlandsbetween mountains and the ocean in Indonesia, is particularly vulnerable to such strikes. This studyaims to mitigate the impact of lightning strikes, particularly in industrial areas like Pasuruan, by delineating lightning-prone areas using a sophisticated methodological approach. Our research employs arobust Ellipse Fitting Method, parameterized with Principal Component Analysis (PCA), to accuratelydefine the boundaries of these high-risk zones. The Ellipse Fitting Method, which involves formingan ellipse from the intersection of a plane and a cone, uses five key parameters: a center point, twovertex points, and two focus points. PCA is then applied to these parameters to determine the ellipse’sconfiguration, with the center point derived from the mean of all data points. The major and minoraxes are defined by the first and second eigenvalues of the principal components, respectively. The sizeof the ellipse correlates with the confidence level, with higher confidence resulting in a larger ellipse.The result of integrating these advanced techniques is the generation of two PCA models from datacollected across 28 sub-districts in Pasuruan, with findings indicating a high level of vulnerability inLumbang District and a moderate level of risk in Gempol District. This methodological framework notonly enhances the precision in identifying lightning-prone areas but also provides a scalable approachfor similar studies in other regions. Suggestion for the further research are to overcome extreme pointsor extreme points in the PCA confidence ellipse such as MVEE.
Analyzing Lightning Strike Susceptibility Using the Elliptical Fitting Method with a Principal Component Analysis Approach Lovytaji, Helmalia A.; Rozikan, Rozikan; Kuncoro, Djati C.; Karisma, Ria Dhea Layla Nur
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3183

Abstract

Lightning is a high-current discharge that occurs in Cumulonimbus clouds, with CG (Cloud to Ground)lightning strikes posing significant dangers, especially to human life. Pasuruan, located in the highlandsbetween mountains and the ocean in Indonesia, is particularly vulnerable to such strikes. This studyaims to mitigate the impact of lightning strikes, particularly in industrial areas like Pasuruan, by delineating lightning-prone areas using a sophisticated methodological approach. Our research employs arobust Ellipse Fitting Method, parameterized with Principal Component Analysis (PCA), to accuratelydefine the boundaries of these high-risk zones. The Ellipse Fitting Method, which involves formingan ellipse from the intersection of a plane and a cone, uses five key parameters: a center point, twovertex points, and two focus points. PCA is then applied to these parameters to determine the ellipse’sconfiguration, with the center point derived from the mean of all data points. The major and minoraxes are defined by the first and second eigenvalues of the principal components, respectively. The sizeof the ellipse correlates with the confidence level, with higher confidence resulting in a larger ellipse.The result of integrating these advanced techniques is the generation of two PCA models from datacollected across 28 sub-districts in Pasuruan, with findings indicating a high level of vulnerability inLumbang District and a moderate level of risk in Gempol District. This methodological framework notonly enhances the precision in identifying lightning-prone areas but also provides a scalable approachfor similar studies in other regions. Suggestion for the further research are to overcome extreme pointsor extreme points in the PCA confidence ellipse such as MVEE.