33 citations as recorded by crossref.

1. An Effective Ionospheric TEC Predicting Approach Using EEMD-PE-Kmeans and Self-Attention LSTM X. Zhao et al. 10.1007/s11063-023-11199-z
2. Statistical Correlation Between DEMETER Satellite Electronic Perturbations and Global Earthquakes With M ≥ 4.8 Y. Zhang et al. 10.1109/TGRS.2023.3265931
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
4. A semi-supervised total electron content anomaly detection method using LSTM-auto-encoder A. Muhammad & F. Külahcı 10.1016/j.jastp.2022.105979
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
7. Firefly Algorithm in detection of TEC seismo-ionospheric anomalies M. Akhoondzadeh 10.1016/j.asr.2015.03.025
8. Ionospheric TEC from the Turkish Permanent GNSS Network (TPGN) and comparison with ARMA and IRI models K. Ansari et al. 10.1007/s10509-017-3159-z
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
11. Pre-Seismic Anomaly Detection from Multichannel Infrared Images of FY-4A Satellite Y. Yue et al. 10.3390/rs15010259
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
13. Seismic classification-based method for recognizing epicenter-neighboring orbits S. Zang et al. 10.1016/j.asr.2017.01.016
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
15. SBAS-Aided GPS Positioning with an Extended Ionosphere Map at the Boundaries of WAAS Service Area M. Kim & J. Kim 10.3390/rs13010151
16. 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
17. 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
18. Investigating short-term earthquake precursors detection through monitoring of total electron content variation in ionosphere N. Zulhamidi et al. 10.3389/fspas.2023.1166394
19. Support Vector Regression model to predict TEC for GNSS signals K. Sivakrishna et al. 10.1007/s11600-022-00954-w
20. Advances in Seismo-LAI anomalies detection within Google Earth Engine (GEE) cloud platform M. Akhoondzadeh 10.1016/j.asr.2022.03.033
21. A Multi-Input Convolutional Neural Networks Model for Earthquake Precursor Detection Based on Ionospheric Total Electron Content H. Uyanık et al. 10.3390/rs15245690
22. GNSS TEC-Based Earthquake Ionospheric Perturbation Detection Using a Novel Deep Learning Framework P. Xiong et al. 10.1109/JSTARS.2022.3175961
23. Feasibility of anomaly occurrence in aerosols time series obtained from MODIS satellite images during hazardous earthquakes M. Akhoondzadeh & F. Jahani Chehrebargh 10.1016/j.asr.2016.05.046
24. 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
25. 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
26. Extending the coverage area of regional ionosphere maps using a support vector machine algorithm M. Kim & J. Kim 10.5194/angeo-37-77-2019
27. Ionospheric characteristics prior to the greatest earthquake in recorded history C. Villalobos et al. 10.1016/j.asr.2015.09.015
28. Ionospheric anomalies detection using autoregressive integrated moving average (ARIMA) model as an earthquake precursor M. Saqib et al. 10.1007/s11600-021-00616-3
29. 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
30. Genetic algorithm for TEC seismo-ionospheric anomalies detection around the time of the Solomon (Mw=8.0) earthquake of 06 February 2013 M. Akhoondzadeh 10.1016/j.asr.2013.04.012
31. 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
32. A review on remotely sensed land surface temperature anomaly as an earthquake precursor A. Bhardwaj et al. 10.1016/j.jag.2017.08.002
33. Support Vector Machine for Regional Ionospheric Delay Modeling Z. Zhang et al. 10.3390/s19132947