Articles | Volume 31, issue 2
https://doi.org/10.5194/angeo-31-173-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/angeo-31-173-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Support vector machines for TEC seismo-ionospheric anomalies detection
M. Akhoondzadeh
Remote Sensing Division, Surveying and Geomatics Engineering Department, University College of Engineering, University of Tehran, Tehran, Iran
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35 citations as recorded by crossref.
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- TEC Anomalies Detection for Qinghai and Yunnan Earthquakes on 21 May 2021 Y. Yue et al. 10.3390/rs14174152
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- Pre-Seismic Anomaly Detection from Multichannel Infrared Images of FY-4A Satellite Y. Yue et al. 10.3390/rs15010259
- Challenges in the Detection of Ionospheric Pre-Earthquake Total Electron Content Anomalies (PETA) for Earthquake Forewarning B. Lim & E. Leong 10.1007/s00024-018-2083-7
- Seismic classification-based method for recognizing epicenter-neighboring orbits S. Zang et al. 10.1016/j.asr.2017.01.016
- Modeling and forecasting of ionosphere TEC using least squares SVM in central Europe S. Ghaffari-Razin et al. 10.1016/j.asr.2022.06.020
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- Earthquake prediction using satellite data: Advances and ahead challenges M. Akhoondzadeh 10.1016/j.asr.2024.06.054
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- Comparison of outliers and novelty detection to identify ionospheric TEC irregularities during geomagnetic storm and substorm A. Pattisahusiwa et al. 10.1088/1742-6596/739/1/012015
- Investigating short-term earthquake precursors detection through monitoring of total electron content variation in ionosphere N. Zulhamidi et al. 10.3389/fspas.2023.1166394
- Support Vector Regression model to predict TEC for GNSS signals K. Sivakrishna et al. 10.1007/s11600-022-00954-w
- Advances in Seismo-LAI anomalies detection within Google Earth Engine (GEE) cloud platform M. Akhoondzadeh 10.1016/j.asr.2022.03.033
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- GNSS TEC-Based Earthquake Ionospheric Perturbation Detection Using a Novel Deep Learning Framework P. Xiong et al. 10.1109/JSTARS.2022.3175961
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- Application of the T2-Hotelling test for investigating ionospheric anomalies before large earthquakes Z. Sadeghi & M. Mashhadi-Hossainali 10.1016/j.jastp.2019.01.010
- Investigation of GPS-TEC measurements using ANN method indicating seismo-ionospheric anomalies around the time of the Chile (Mw=8.2) earthquake of 01 April 2014 M. Akhoondzadeh 10.1016/j.asr.2014.07.013
- Extending the coverage area of regional ionosphere maps using a support vector machine algorithm M. Kim & J. Kim 10.5194/angeo-37-77-2019
- Ion Transport from Soil to Air and Electric Field Amplitude of the Boundary Layer A. Muhammad et al. 10.1134/S0016793223600613
- Ionospheric characteristics prior to the greatest earthquake in recorded history C. Villalobos et al. 10.1016/j.asr.2015.09.015
- Ionospheric anomalies detection using autoregressive integrated moving average (ARIMA) model as an earthquake precursor M. Saqib et al. 10.1007/s11600-021-00616-3
- Regional modeling and forecasting of precipitable water vapor using least square support vector regression S. Ghaffari-Razin et al. 10.1016/j.asr.2023.01.030
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- Support Vector Machine for Regional Ionospheric Delay Modeling Z. Zhang et al. 10.3390/s19132947
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