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.
Regular paper
| 
06 Feb 2013
Regular paper |
| 06 Feb 2013
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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Cited
38 citations as recorded by crossref.
- An Effective Ionospheric TEC Predicting Approach Using EEMD-PE-Kmeans and Self-Attention LSTM X. Zhao et al. https://doi.org/10.1007/s11063-023-11199-z
- Statistical Correlation Between DEMETER Satellite Electronic Perturbations and Global Earthquakes With M ≥ 4.8 Y. Zhang et al. https://doi.org/10.1109/TGRS.2023.3265931
- Decision Tree, Bagging and Random Forest methods detect TEC seismo-ionospheric anomalies around the time of the Chile, (M= 8.8) earthquake of 27 February 2010 M. Akhoondzadeh https://doi.org/10.1016/j.asr.2016.03.035
- A semi-supervised total electron content anomaly detection method using LSTM-auto-encoder A. Muhammad & F. Külahcı https://doi.org/10.1016/j.jastp.2022.105979
- Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods M. Ghaffari Razin & B. Voosoghi https://doi.org/10.1016/j.asr.2022.01.003
- Comparisons of autoregressive integrated moving average (ARIMA) and long short term memory (LSTM) network models for ionospheric anomalies detection: a study on Haiti (Mw = 7.0) earthquake M. Saqib et al. https://doi.org/10.1007/s40328-021-00371-3
- Firefly Algorithm in detection of TEC seismo-ionospheric anomalies M. Akhoondzadeh https://doi.org/10.1016/j.asr.2015.03.025
- Ionospheric TEC from the Turkish Permanent GNSS Network (TPGN) and comparison with ARMA and IRI models K. Ansari et al. https://doi.org/10.1007/s10509-017-3159-z
- TEC Anomalies Detection for Qinghai and Yunnan Earthquakes on 21 May 2021 Y. Yue et al. https://doi.org/10.3390/rs14174152
- Thermal and TEC anomalies detection using an intelligent hybrid system around the time of the Saravan, Iran, (M= 7.7) earthquake of 16 April 2013 M. Akhoondzadeh https://doi.org/10.1016/j.asr.2013.12.017
- Pre-Seismic Anomaly Detection from Multichannel Infrared Images of FY-4A Satellite Y. Yue et al. https://doi.org/10.3390/rs15010259
- Challenges in the Detection of Ionospheric Pre-Earthquake Total Electron Content Anomalies (PETA) for Earthquake Forewarning B. Lim & E. Leong https://doi.org/10.1007/s00024-018-2083-7
- Seismic classification-based method for recognizing epicenter-neighboring orbits S. Zang et al. https://doi.org/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. https://doi.org/10.1016/j.asr.2022.06.020
- SBAS-Aided GPS Positioning with an Extended Ionosphere Map at the Boundaries of WAAS Service Area M. Kim & J. Kim https://doi.org/10.3390/rs13010151
- Earthquake prediction using satellite data: Advances and ahead challenges M. Akhoondzadeh https://doi.org/10.1016/j.asr.2024.06.054
- An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan, Iran, (M= 6.4) earthquake of 11 August 2012 M. Akhoondzadeh https://doi.org/10.1016/j.asr.2013.05.024
- Comparison of outliers and novelty detection to identify ionospheric TEC irregularities during geomagnetic storm and substorm A. Pattisahusiwa et al. https://doi.org/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. https://doi.org/10.3389/fspas.2023.1166394
- Support Vector Regression model to predict TEC for GNSS signals K. Sivakrishna et al. https://doi.org/10.1007/s11600-022-00954-w
- Advances in Seismo-LAI anomalies detection within Google Earth Engine (GEE) cloud platform M. Akhoondzadeh https://doi.org/10.1016/j.asr.2022.03.033
- A Multi-Input Convolutional Neural Networks Model for Earthquake Precursor Detection Based on Ionospheric Total Electron Content H. Uyanık et al. https://doi.org/10.3390/rs15245690
- Evaluating Ionospheric Total Electron Content (TEC) Variations as Precursors to Seismic Activity: Insights from the 2024 Noto Peninsula and Nichinan Earthquakes of Japan K. Nayak et al. https://doi.org/10.3390/atmos15121492
- GNSS TEC-Based Earthquake Ionospheric Perturbation Detection Using a Novel Deep Learning Framework P. Xiong et al. https://doi.org/10.1109/JSTARS.2022.3175961
- Feasibility of anomaly occurrence in aerosols time series obtained from MODIS satellite images during hazardous earthquakes M. Akhoondzadeh & F. Jahani Chehrebargh https://doi.org/10.1016/j.asr.2016.05.046
- Application of the T2-Hotelling test for investigating ionospheric anomalies before large earthquakes Z. Sadeghi & M. Mashhadi-Hossainali https://doi.org/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 (M= 8.2) earthquake of 01 April 2014 M. Akhoondzadeh https://doi.org/10.1016/j.asr.2014.07.013
- TEC Variability during Fluctuating Events at Koudougou Station during Solar Cycle 24 T. Pahima et al. https://doi.org/10.4236/ijg.2022.1310047
- Artificial Intelligence and Total Electron Content in Earthquake-Related Seismo-Ionospheric Analysis: A Mapping Review F. Díaz et al. https://doi.org/10.3390/geomatics6040074
- Extending the coverage area of regional ionosphere maps using a support vector machine algorithm M. Kim & J. Kim https://doi.org/10.5194/angeo-37-77-2019
- Ion Transport from Soil to Air and Electric Field Amplitude of the Boundary Layer A. Muhammad et al. https://doi.org/10.1134/S0016793223600613
- Ionospheric characteristics prior to the greatest earthquake in recorded history C. Villalobos et al. https://doi.org/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. https://doi.org/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. https://doi.org/10.1016/j.asr.2023.01.030
- Genetic algorithm for TEC seismo-ionospheric anomalies detection around the time of the Solomon (M= 8.0) earthquake of 06 February 2013 M. Akhoondzadeh https://doi.org/10.1016/j.asr.2013.04.012
- A Multi-Network based Hybrid LSTM model for ionospheric anomaly detection: A case study of the Mw 7.8 Nepal earthquake E. Şentürk et al. https://doi.org/10.1016/j.asr.2022.04.057
- A review on remotely sensed land surface temperature anomaly as an earthquake precursor A. Bhardwaj et al. https://doi.org/10.1016/j.jag.2017.08.002
- Support Vector Machine for Regional Ionospheric Delay Modeling Z. Zhang et al. https://doi.org/10.3390/s19132947
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