Open Access
| Issue |
EPJ Web Conf.
Volume 369, 2026
4th International Conference on Artificial Intelligence and Applied Mathematics (JIAMA’26)
|
|
|---|---|---|
| Article Number | 01010 | |
| Number of page(s) | 11 | |
| Section | Applied Physics & Engineering Systems Modeling | |
| DOI | https://doi.org/10.1051/epjconf/202636901010 | |
| Published online | 13 May 2026 | |
- CHERKAOUI et ASEBRIY, 2003. Le risque sismique dans le Nord du Maroc. Trav. Inst. Sci. Rabat, sér. Géol. & Géogr. phys., n° 21, 2003, p.225–232 [Google Scholar]
- Mignan A, Broccardo M (2020) Neural network applications in earthquake prediction (1994–2019): Metaanalytic and statistical insights on their limitations. Seismol Res Lett 91(4):2330–2342 [Google Scholar]
- Carlson JM, Langer JS, Shaw BE (1994) Dynamics of earthquake faults. Rev Modern Physics 66(2):657 [Google Scholar]
- Gulia L, Wiemer S (2019) Real-time discrimination of earthquake foreshocks and aftershocks. Nature 574(7777):193–199 [Google Scholar]
- Linville L, Pankow K, Draelos T (2019) Deep learning models augment analyst decisions for event discrimination. Geophys Res Lett 46(7):3643–3651 [Google Scholar]
- Meier M-A, Ross ZE, Ramachandran A, Balakrishna A, Nair S, Kundzicz P, Li Z, Andrews J, Hauksson E, Yue Y (2019) Reliable real-time seismic signal/noise discrimination with machine learning. J Geophys Res: Solid Earth 124(1):788–800 [Google Scholar]
- Li S, Xu W, Li Z (2022) Review of the SBAS InSAR Time-series algorithms, applications, and challenges. Geodesy Geodyn 13(2):114–126 [Google Scholar]
- Mousavi SM, Ellsworth WL, Zhu W, Chuang LY, Beroza GC (2020) Earthquake transformer–an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nature Commun 11(1):3952 [Google Scholar]
- Ambrosino F, Thinová L, Briestenský M, Šebela S, Sabbarese C (2020) Detecting time series anomalies using hybrid methods applied to Radon signals recorded in caves for possible correlation with earthquakes. Acta Geodaetica et Geophysica 55:405–420 [Google Scholar]
- Mandrikova O, Fetisova N, Polozov Y (2021) Hybrid model for time series of complex structure with ARIMA components. Mathematics 9(10):1122 [Google Scholar]
- Gao Y, Mosalam KM, Chen Y, Wang W, Chen Y (2021) Auto-regressive integrated moving-average machine [Google Scholar]
- Amei A, Fu W, Ho C-H (2012) Time series analysis for predicting the occurrences of large scale earthquakes. Int J App Sci Tech 2(7):75 [Google Scholar]
- Musarat MA, Alaloul WS, Rabbani MBA, Ali M, Altaf M, Fediuk R, Vatin N, Klyuev S, Bukhari H, Sadiq A (2021) Kabul river flow prediction using automated ARIMA forecasting: A machine learning approach. Sustainability 13(19):10720 [Google Scholar]
- https://www.ign.es/web/en/ign/portal/sis-catalogo-terremotos [Google Scholar]
- Wenwen Hou, 2026 Statistical and machine learning methods for multi-step earthquake frequency forecasting in indonesian regions https://doi.org/10.1007/s11069-025-07744-9 [Google Scholar]
- Kijko A, Smit A (2017) Estimation of the frequency-magnitude Gutenberg-Richter b-value without making assumptions on levels of completeness. Seismol Res Lett 88(2A):311–318 [Google Scholar]
- Al Banna MH, Taher KA, Kaiser MS, Mahmud M, Rahman MS, Hosen AS, Cho GH (2020) Application of artificial intelligence in predicting earthquakes: state-of-the-art and future challenges. IEEE Access 8:192880–192923 [Google Scholar]
- Aljarah Ibrahim and al (2018) Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cognitive Comput 10(3):478–495 [Google Scholar]
- Ghosh R, Sinha N, Biswas SK (2019) Automated eye blink artefact removal from EEG using support vector machine and autoencoder. IET Signal Process 13(2):141–148 [Google Scholar]
- Rigatti SJ (2017) Random forest. Journal of Insurance Medicine 47(1):31–39. ISBN: 0743-6661 Publisher:American Academy of Insurance Medicine 1700 Magnavox Way. Fort Wayne, IN, p 46804 [Google Scholar]
- Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794. [Google Scholar]
- Staudemeyer RC, Morris ER (2019) Understanding LSTM–a tutorial into long short-term memory recurrent neural networks. arXiv arXiv:1909.09586 American Academy of Insurance Medicine 1700 Magnavox Way. Fort Wayne, IN, p 46804 [Google Scholar]
- Sahoo BB, Jha R, Singh A, Kumar D (2019) Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting. Acta Geophysica 67(5):1471–1481 [Google Scholar]
- Zhang M (2018) Time series: Autoregressive models ar, ma, arma, arima. University of Pittsburgh [Google Scholar]
- Fattah J, Ezzine L, Aman Z, El Moussami H, Lachhab A (2018) Forecasting of demand using ARIMA model. Int J Engi Bus Manage 10:1847979018808673 [Google Scholar]
- Júnior DSdOS, Oliveira JF, Mattos Neto PS (2019) An intelligent hybridization of ARIMA with machine learning models for time series forecasting. Knowl-Based Syst 175:72–86 learning for damage identification of steel frames. Appl Sci 11(13):6084 [Google Scholar]
- Wilson J, Hutter F, Deisenroth M (2018) Maximizing acquisition functions for Bayesian optimization. Advances in neural information processing systems 31 [Google Scholar]
- Meng H, Geng M, Han T (2023) Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis. Reliab Engin & Syst Safety 236:109288 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

