EARTHQUAKE FORECASTING USING ARTIFICIAL NEURAL NETWORKS

18 citations as recorded by crossref.

1. Assessment on maximum magnitude of natural and triggered earthquake when water is impounded in the mining pit: A case study in Nui Nho quarry, Vietnam based on gravity and magnetic data X. Bui et al. https://doi.org/10.2205/2020ES000690
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3. Modelling the seismic activity of Kahramanmaraş, Türkiye with recurrent neural network (RNN) and long short-term memory (LSTM) methods O. Aydin et al. https://doi.org/10.1007/s11069-025-07520-9
4. A synoptic view of the natural time distribution and contemporary earthquake hazards in Sumatra, Indonesia S. Pasari et al. https://doi.org/10.1007/s11069-021-04682-0
5. An improved third term backpropagation algorithm – inertia expanded chebyshev orthogonal polynomial M. Sornam & V. Vanitha https://doi.org/10.3233/JIFS-190063
6. Predicting the Unpredictable: Advancements in Earthquake Forecasting Using Artificial Intelligence and LSTM Networks . Sevim Bilici et al. https://doi.org/10.1134/S0016793224600693
7. Graph neural ordinary differential equations for epidemic forecasting X. Yanqin et al. https://doi.org/10.1007/s42486-024-00161-0
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9. Contemporary Earthquake Hazards in the West-Northwest Himalaya: A Statistical Perspective through Natural Times S. Pasari & Y. Sharma https://doi.org/10.1785/0220200104
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17. Inverse Gaussian versus lognormal distribution in earthquake forecasting: keys and clues S. Pasari https://doi.org/10.1007/s10950-019-09822-5
18. The Current State of Earthquake Potential on Java Island, Indonesia S. Pasari et al. https://doi.org/10.1007/s00024-021-02781-4