@inproceedings{09dc63c505c440a78d247ea997a2bffd,
title = "2023 Kahramanmaras Depremleri Sirasinda Olusan Iyonk re Bozulmalarinin Rastgele Ormanlar Algoritmasi Kullanilarak Tespiti",
abstract = "In this study, a new approach based on Random Forest algorithm is presented for the detection of earthquake precursors in the ionosphere. Total Electron Content (TEC) data estimated from TUSAGA-Active stations belonging to three quiet and three disturbed days and the 2023 Kahramanmara{\c s} earthquake period are used in the study. 9 different features derived from TEC data are used in the proposed model. Random Forest algorithm successfully has detected earthquake-related ionospheric disturbances with 95.45\% Accuracy rate. It is observed that the model can also effectively distinguish disturbances caused by solar activity and geomagnetic storms.",
keywords = "Ionospheric Earthquake Precursors, Machine Learning, Random Forests, TEC Disturbances",
author = "Oncul, \{Makbule Hilal Mutevelli\} and Zorkun, \{Nazlican Gengec\} and Secil Karatay and Faruk Erken and Feza Arikan",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 ; Conference date: 25-06-2025 Through 28-06-2025",
year = "2025",
doi = "10.1109/SIU66497.2025.11112232",
language = "T{\"u}rk{\c c}e",
series = "33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 - Proceedings",
address = "!!United States",
}