26 citations as recorded by crossref.

1. The Theoretical and Practical Foundations of Strong Earthquake Predictability O. Elshin & A. Tronin https://doi.org/10.4236/ojer.2021.102002
2. On the Relation Between Anomalous Clouds and Earthquakes in Italian Land G. Guangmeng https://doi.org/10.3389/feart.2022.812540
3. Atmospheric Anomaly Analysis Related to Ms > 6.0 Earthquakes in China during 2020–2021 X. Xu et al. https://doi.org/10.3390/rs13204052
4. Geo-sensor(s) for potential prediction of earthquakes: can earthquake be predicted by abnormal animal phenomena? K. Cao & Q. Huang https://doi.org/10.1080/19475683.2018.1450785
5. Climatic and seismic data-driven deep learning model for earthquake magnitude prediction B. Sadhukhan et al. https://doi.org/10.3389/feart.2023.1082832
6. Impact of climate change on seismicity:a statistical approach C. Maji et al. https://doi.org/10.1007/s12517-021-08946-8
7. Forecasting natural hazards, performance of scientists, ethics, and the need for transparency F. Guzzetti https://doi.org/10.1080/02772248.2015.1030664
8. Processes Leading to a Localized Decrease in Air Humidity Prior to the Earthquake A. Nikitin et al. https://doi.org/10.1134/S1990793126700302
9. Synoptic detection of the short-term atmospheric precursors prior to a major earthquake in the Middle East, North Saravan M 7.8 earthquake, SE Iran M. Mansouri Daneshvar et al. https://doi.org/10.1007/s11869-013-0214-y
10. Geoelectric studies in earthquake hazard assessment: the case of the Kozlodui nuclear power plant, Bulgaria S. Kovacikova et al. https://doi.org/10.1007/s11069-024-06867-9
11. Brief Communication: Earthquake–cloud coupling through the global atmospheric electric circuit R. Harrison et al. https://doi.org/10.5194/nhess-14-773-2014
12. Thermal anomalies detection before Saravan earthquake (April 16th, 2013, MW = 7.8) using time series method, satellite, and meteorological data M. Khalili et al. https://doi.org/10.1007/s12040-019-1286-3
13. Aerosols and seismo-ionosphere coupling: A review A. Namgaladze et al. https://doi.org/10.1016/j.jastp.2018.01.014
14. Influences of multiple layers of air temperature differences on tidal forces and tectonic stress before, during and after the Jiujiang earthquake M. Weiyu et al. https://doi.org/10.1016/j.rse.2018.03.003
15. On the detection of anomalous seismo-ionospheric behavior in the presence of space weather stimuli for large earthquakes B. Lim & E. Leong https://doi.org/10.1016/j.asr.2018.10.020
16. Geosystemics View of Earthquakes A. De Santis et al. https://doi.org/10.3390/e21040412
17. A Retrospective Analysis about the Italy Emilia M6.0 Earthquake Prediction G. Guo https://doi.org/10.4236/ojer.2021.102005
18. Geospace perturbations induced by the Earth: The state of the art and future trends A. De Santis et al. https://doi.org/10.1016/j.pce.2015.05.004
19. On a report that the 2012 M 6.0 earthquake in Italy was predicted after seeing an unusual cloud formation J. Thomas et al. https://doi.org/10.5194/nhess-15-1061-2015
20. Detection and Identification of Preseismic Thermal Anomalies in Cloudy Conditions Associated With the 2022 Luding (China) Mw 6.6 Earthquake F. Jing et al. https://doi.org/10.1109/TGRS.2023.3304975
21. Classifiers for Ground-Based Cloud Images Using Texture Features T. Kliangsuwan & A. Heednacram https://doi.org/10.4028/www.scientific.net/AMR.931-932.1392
22. Investigating the relationship between earthquake occurrences and climate change using RNN-based deep learning approach B. Sadhukhan et al. https://doi.org/10.1007/s12517-021-09229-y
23. Conduction current and extraneous electric current in the global electric circuit A. Namgaladze & M. Karpov https://doi.org/10.1134/S1990793115050231
24. Determining Neuronal Number in Each Hidden Layer Using Earthquake Catalogues as Training Data in Training an Embedded Back Propagation Neural Network for Predicting Earthquake Magnitude J. Lin et al. https://doi.org/10.1109/ACCESS.2018.2870189
25. Analysis of Atmospheric Precursors at the Time of the Earthquake Using Satellite Images S. Salimi et al. https://doi.org/10.1007/s12524-024-02023-9
26. Using Robust Satellite Technique (RST) to determine thermal anomalies before a strong earthquake: A case study of the Saravan earthquake (April 16th, 2013, MW = 7.8, Iran) M. Khalili et al. https://doi.org/10.1016/j.jseaes.2019.01.009