Advanced Predictive Models for Natural Disasters
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-65549-4_5
Reference20 articles.
1. M.H. Al Banna, K.A. Taher, M.S. Kaiser, M. Mahmud, M.S. Rahman, A.S. Hosen, G.H. Cho, Application of artificial intelligence in predicting earthquakes: state-of-the-art and future challenges. IEEE Access 8, 192880–192923 (2020). https://doi.org/10.1109/ACCESS.2020.3029859
2. G. Cremen, C. Galasso, Earthquake early warning: recent advances and perspectives. Earth Sci. Rev. 205, 103184 (2020). https://doi.org/10.1016/j.earscirev.2020.103184
3. K.W. Campbell, Proposed methodology for estimating the magnitude at which subduction megathrust ground motions and source dimensions exhibit a break in magnitude scaling: example for 79 global subduction zones. Earthq. Spectra 36(3), 1271–1297 (2020). https://doi.org/10.1177/8755293019899957
4. M.S. Abdalzaher, H.A. Elsayed, M.M. Fouda, M.M. Salim, Employing machine learning and iot for earthquake early warning system in smart cities. Energies 16(1), 495 (2023). https://doi.org/10.3390/en16010495
5. L. Xu, P. Gober, H.S. Wheater, Y. Kajikawa, Reframing socio-hydrological research to include a social science perspective. J. Hydrol. 563, 76–83 (2018). https://doi.org/10.1016/j.jhydrol.2018.05.061
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