Abstract
For decades, earthquake prediction has been the focus of research using various methods and techniques. It is difficult to predict the size and location of the next earthquake after one has occurred. However, machine learning (ML)-based approaches and methods have shown promising results in earthquake prediction over the past few years. Thus, we compiled 31 studies on earthquake prediction using ML algorithms published from 2017 to 2021, with the aim of providing a comprehensive review of previous research. This study covered different geographical regions globally. Most of the models analysed in this study are keen on predicting the earthquake magnitude, trend and occurrence. A comparison of different types of seismic indicators and the performance of the algorithms were summarized to identify the best seismic indicators with a high-performance ML algorithm. Towards this end, we have discussed the highest performance of the ML algorithm for earthquake magnitude prediction and suggested a potential algorithm for future studies.
Similar content being viewed by others
Data availability
Not Applicable.
Code availability
Not Applicable.
References
Al Banna MH, Taher KA, Kaiser MS, Mahmud M, Rahman MS, Hosen ASMS, Cho GH (2020) Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges. IEEE Access 8:192880–192923. https://doi.org/10.1109/ACCESS.2020.3029859
Asencio-Cortés G, Morales-Esteban A, Shang X, Martínez-Álvarez F (2018) Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure. Comput Geosci 115(September 2017):198–210. https://doi.org/10.1016/j.cageo.2017.10.011
Asim KM, Martínez-Álvarez F, Basit A, Iqbal T (2017) Earthquake magnitude prediction in Hindukush region using machine learning techniques. Nat Hazards 85(1):471–486. https://doi.org/10.1007/s11069-016-2579-3
Asim KM, Idris A, Iqbal T, Martínez-Álvarez F (2018a) Earthquake prediction model using support vector regressor and hybrid neural networks. PLoS ONE 13(7):1–22. https://doi.org/10.1371/journal.pone.0199004
Asim KM, Idris A, Iqbal T, Martínez-Álvarez F (2018b) Seismic indicators based earthquake predictor system using Genetic Programming and AdaBoost classification. Soil Dyn Earthq Eng 111(February):1–7. https://doi.org/10.1016/j.soildyn.2018.04.020
Asim, Khawaja M, Moustafa, SS, Niaz, IA, Elawadi, EA, Iqbal, T, Martínez-Álvarez, F (2020) Seismicity analysis and machine learning models for short-term low magnitude seismic activity predictions in Cyprus. Soil Dynamics and Earthquake Engineering, 130(October 2019). https://doi.org/10.1016/j.soildyn.2019.105932
Bao Z, Zhao J, Huang P, Yong S, Wang X (2021) A deep learning-based electromagnetic signal for earthquake magnitude prediction. Sens, 21(13). https://doi.org/10.3390/s21134434
Cao C, Wu X, Yang L, Zhang Q, Wang X, Yuen DA (2021) Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog. Sustain (Switzerland), 9–14. https://doi.org/10.3390/su13094905
Chanda S, Raghucharan MC, Karthik Reddy KSK, Chaudhari V, Somala SN (2021) Duration prediction of Chilean strong motion data using machine learning. J South Am Earth Sci, 109(October 2020). https://doi.org/10.1016/j.jsames.2021.103253
Cheraghi A, Ghanbari A (2017) Study of risk analysis and earthquake magnitude and timing prediction via tectonic and geotechnical properties of the faults and identifying risky areas in terms of seismicity in larestan city using artificial neural network. QUID: Investigación, Ciencia y Tecnología, No. Extra 1, 2017, Págs. 1137-1142, (1). Retrieved from https://dialnet.unirioja.es/servlet/articulo?codigo=6158766&info=resumen&idioma=ENG. https://dialnet.unirioja.es/servlet/articulo?codigo=6158766. Accessed 10 Oct 2021
Cicerone RD, Ebel JE, Britton J (2009) A systematic compilation of earthquake precursors. Tectonophysics 476(3):371–396. https://doi.org/10.1016/j.tecto.2009.06.008
Corbi F, Sandri L, Bedford J, Funiciello F, Brizzi S, Rosenau M, Lallemand S (2019) Machine Learning Can Predict the Timing and Size of Analog Earthquakes. Geophys Res Lett 46(3):1303–1311. https://doi.org/10.1029/2018GL081251
Debnath P, Chittora P, Chakrabarti T, Chakrabarti P, Leonowicz Z, Jasinski M, Gono R, Jasińska E (2021) Analysis of earthquake forecasting in India using supervised machine learning classifiers. Sustainability (switzerland) 13(2):1–13. https://doi.org/10.3390/su13020971
Essam, Y, Kumar, P, Ahmed, AN, Murti, MA, El-Shafie, A (2021) Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia. Soil Dyn Earthquake Eng, 147(May). https://doi.org/10.1016/j.soildyn.2021.106826
Fernández-Gómez, MJ, Asencio-Cortés, G, Troncoso, A, Martínez-álvarez, F (2017) Large earthquake magnitude prediction in Chile with imbalanced classifiers and ensemble learning. Appl Sci (Switzerland), 7(6). https://doi.org/10.3390/app7060625
Florido E, Asencio-Cortés G, Aznarte JL, Rubio-Escudero C, Martínez-Álvarez F (2018) A novel tree-based algorithm to discover seismic patterns in earthquake catalogs. Comput Geosci 115(March):96–104. https://doi.org/10.1016/j.cageo.2018.03.005
Hajikhodaverdikhan P, Nazari M, Mohsenizadeh M, Shamshirband S, Chau K (2018) Earthquake prediction with meteorological data by particle filter-based support vector regression. Engineering Applications of Computational Fluid Mechanics 2060:679–688. https://doi.org/10.1080/19942060.2018.1512010
Huang JP, Wang XA, Zhao Y, Xin C, Xiang H (2018) Large earthquake magnitude prediction in Taiwan based on deep learning neural network. Neural Netw World 28(2):149–160. https://doi.org/10.14311/NNW.2018.28.009
Karimzadeh S, Matsuoka M, Kuang J, Ge L (2019) Spatial Prediction of Aftershocks Triggered by a Major Earthquake : A Binary Machine Learning Perspective. International Journal of Geo-Information Article 8(10):462
Khosravikia F, Clayton P (2021) Machine learning in ground motion prediction. Comput Geosci, 148(June 2020). https://doi.org/10.1016/j.cageo.2021.104700
Kubo H, Kunugi T, Suzuki W, Suzuki S, Aoi S (2020) Hybrid predictor for ground-motion intensity with machine learning and conventional ground motion prediction equation. Sci Rep 10(1):1–12. https://doi.org/10.1038/s41598-020-68630-x
Lin J, Chao C, Chiou J (2018) 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. IEEE Access 6:52582–52597. https://doi.org/10.1109/ACCESS.2018.2870189
Majhi SK, Hossain S, Padhi T (2020) MFOFLANN : moth flame optimized functional link artificial neural network for prediction of earthquake magnitude. Evolving Syst, 1997. https://doi.org/10.1007/s12530-019-09293-6
Murwantara IM, Yugopuspito P, Hermawan R (2020) Comparison of machine learning performance for earthquake prediction in Indonesia using 30 years historical data. Telkomnika (Telecommun Comput Electron Control) 18(3):1331–1342. https://doi.org/10.12928/TELKOMNIKA.v18i3.14756
Rafiei MH, Adeli H (2017) NEEWS: A novel earthquake early warning model using neural dynamic classification and neural dynamic optimization. Soil Dyn Earthq Eng 100(February):417–427. https://doi.org/10.1016/j.soildyn.2017.05.013
Rahmat B, Joelianto E, Afiadi F, Fandenza ADL, Kurniawan RA, Puspaningrum EY, Nugroho B, Kartika DSY (2020) Comparison of B-Value Predictions as Earthquake Precursors using Extreme Learning Machine and Deep Learning. Int Indonesia J 12(1):47–52. Retrieved from https://www.researchgate.net/publication/349517987_Comparison_of_B-Value_Predictions_as_Earthquake_Precursors_using_Extreme_Learning_Machine_and_Deep_Learning. Accessed 10 Oct 2021
Rouet-Leduc B, Hulbert C, Lubbers N, Barros K, Humphreys CJ, Johnson PA (2017) Machine Learning Predicts Laboratory Earthquakes. Geophys Res Lett 44:9276–9282
Salam MA, Ibrahim L, Abdelminaam DS (2021) Earthquake Prediction using Hybrid Machine Learning Techniques. Int J Adv Comput Sci Appl 12(5):654–665. https://doi.org/10.14569/IJACSA.2021.0120578
Shodiq MN, Kusuma DH, Rifqi MG (2018) Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia. INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION 2:37–43
Vardaan K, Bhandarkar T, Satish N, Sridhar S, Sivakumar R, Ghosh S (2019) Earthquake trend prediction using long short-term memory RNN. Int J Electric Comput Eng 9(2):1304–1312. https://doi.org/10.11591/ijece.v9i2.pp1304-1312
Wang Y, Wang Z, Cao Z, Lan J (2017) Deep learning for magnitude prediction in earthquake early warning. IEEE Trans Emerg Top Comput 8(1):148–158. https://doi.org/10.1109/TETC.2017.2699169
Xiong P, Tong L, Zhang K, Shen X, Battiston R, Ouzounov D, Iuppa R, Crookes D, Long C, Zhou H (2021) Towards advancing the earthquake forecasting by machine learning of satellite data. Sci Total Environ, 771. https://doi.org/10.1016/j.scitotenv.2021.145256
Yousefzadeh M, Hosseini SA, Farnaghi M (2021) Spatiotemporally explicit earthquake prediction using deep neural network. Soil Dyn Earthquake Eng, 144(February). https://doi.org/10.1016/j.soildyn.2021.106663
Funding
This work was supported by the “Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme with Project Code: FRGS/1/2020/STG07/USM/03/3.”
Author information
Authors and Affiliations
Contributions
Siti Harwani Md Yusof provided the idea of writing a review paper, reviewed and edited the draft. Nurafiqah Syahirah Md Ridzwan performed the literature search, data analysis and wrote the initial draft.
Corresponding author
Ethics declarations
Ethics
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing Interest
The authors have no competing interests to declare that are relevant to the content of this article.
Additional information
Communicated by: H. Babaie.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ridzwan, N.S.M., Yusoff, S.H.M. Machine learning for earthquake prediction: a review (2017–2021). Earth Sci Inform 16, 1133–1149 (2023). https://doi.org/10.1007/s12145-023-00991-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-023-00991-z