35 citations as recorded by crossref.

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3. 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 10.1016/j.asr.2016.03.035
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5. Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods M. Ghaffari Razin & B. Voosoghi 10.1016/j.asr.2022.01.003
6. 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. 10.1007/s40328-021-00371-3
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9. TEC Anomalies Detection for Qinghai and Yunnan Earthquakes on 21 May 2021 Y. Yue et al. 10.3390/rs14174152
10. Thermal and TEC anomalies detection using an intelligent hybrid system around the time of the Saravan, Iran, (Mw=7.7) earthquake of 16 April 2013 M. Akhoondzadeh 10.1016/j.asr.2013.12.017
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12. 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
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14. 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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17. An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan, Iran, (Mw=6.4) earthquake of 11 August 2012 M. Akhoondzadeh 10.1016/j.asr.2013.05.024
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20. Support Vector Regression model to predict TEC for GNSS signals K. Sivakrishna et al. 10.1007/s11600-022-00954-w
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25. 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
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33. 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. 10.1016/j.asr.2022.04.057
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35. Support Vector Machine for Regional Ionospheric Delay Modeling Z. Zhang et al. 10.3390/s19132947