LIGHTNING–METEOROLOGY RELATIONSHIPS OVER SRI LANKA AND INDONESIA: A MACHINE LEARNING APPROACH

Nandivada Umakanth; Rajesh Gogineni; Kalyankar  Madan Mohan Rao; Bollareddy Revanth Reddy; Kondaveeti SivaKrishna; Yarlagadda Ramakrishna; Myla Chimpiri Rao

doi:10.29121/ijoest.v9.i3.2025.691

Authors

Nandivada Umakanth Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune - 411008, India
Rajesh Gogineni Department of ECE, Dhanekula Institute of Engineering and Technology, Vijayawada–521139, India https://orcid.org/0000-0001-5812-0038
Kalyankar Madan Mohan Rao Department of CSE-AIML, MLR Institute of Technology, Hyderabad, Telangana-500043, India
Bollareddy Revanth Reddy Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune - 411008, India
Kondaveeti SivaKrishna Department of CSE-AIML, MLR Institute of Technology, Hyderabad, Telangana-500043, India https://orcid.org/0000-0001-5812-846X
Yarlagadda Ramakrishna Department of ECE, Dhanekula Institute of Engineering and Technology, Vijayawada–521139, India
Myla Chimpiri Rao Department of Physics, Andhra Loyola College, Vijayawada-520008, India https://orcid.org/0000-0001-9136-9679

DOI:

https://doi.org/10.29121/ijoest.v9.i3.2025.691

Keywords:

Lightning, Cloud, Regression, Model

Abstract

The relationship between lightning flashes (LF) and various meteorological parameters is analyzed using lightning data from 1995 to 2014. The meteorological parameters considered in this study include aerosol optical depth (AOD), precipitation (P), relative humidity (Rh), convective available potential energy (CAPE), effective cloud droplet size (CER), total precipitable water (TPW), cloud fraction (CF), cloud top temperature (CTT), Richardson number (RN), cloud ice water content (CIWC), and cloud liquid water content (CLWC). This study examines two regions with distinct climates: Sri Lanka (R1) and Indonesia (R2). Results show lower lightning activity in R1 (15.5 flashes/km²/year) than in R2 (21.8 flashes/km²/year), with both peaking in April. Furthermore, the study evaluates the effectiveness of different regression techniques in modeling lightning activity. The Support Vector (SV) regression model performs best for Sri Lanka, while the Random Forest (RF) regression model emerges as the most suitable approach for Indonesia.

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