<strong>Machine Learning–Aided Extreme Total Electron Content Prediction Using Extreme Value Theory: Insights from Long-Term GNSS Data</strong>

Venkata  Ratnam Devanaboyina

Presentation information

[E] Oral

P (Space and Planetary Sciences ) » P-EM Solar-Terrestrial Sciences, Space Electromagnetism & Space Environment

[P-EM16] Coupling Processes in the Atmosphere-Ionosphere System

Mon. May 25, 2026 1:45 PM - 3:15 PM 304 (International Conference Hall, Makuhari Messe)

Chairperson:Otsuka Yuichi(Institute for Space-Earth Environmental Research, Nagoya University), Chang Loren(Department of Space Science and Engineering, National Central University)

3:00 PM - 3:15 PM

[PEM16-30] Machine Learning–Aided Extreme Total Electron Content Prediction Using Extreme Value Theory: Insights from Long-Term GNSS Data

★Invited Papers

*Venkata Ratnam Devanaboyina¹, Yuichi Otsuka², Suneetha Emmela¹, Atsuki Shinbori², Michi Nishioka³, Septi Perwitasari³ (1. KLEF deemed to be University, India, 2. Institute for Space-Earth Environmental Research, Nagoya University, Japan, 3. National Institute of Information and Communications Technology, Tokyo, Japan)

Keywords:ionosphere, Global Satellite Navigation System, Total Electro Content, Extreme value theory, Machine learning for space weather