SmartCity 360 2016, SmartCity 360 2015: Smart City 360° pp 468-478 | Cite as

Seismic Source Modeling by Clustering Earthquakes and Predicting Earthquake Magnitudes

  • Mahdi Hashemi
  • Hassan A. Karimi
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 166)


Seismic sources are currently generated manually by experts, a process which is not efficient as the size of historical earthquake databases is growing. However, large historical earthquake databases provide an opportunity to generate seismic sources through data mining techniques. In this paper, we propose hierarchical clustering of historical earthquakes for generating seismic sources automatically. To evaluate the effectiveness of clustering in producing homogenous seismic sources, we compare the accuracy of earthquake magnitude prediction models before and after clustering. Three prediction models are experimented: decision tree, SVM, and kNN. The results show that: (1) the clustering approach leads to improved accuracy of prediction models; (2) the most accurate prediction model and the most homogenous seismic sources are achieved when earthquakes are clustered based on their non-spatial attributes; and (3) among the three prediction models experimented in this work, decision tree is the most accurate one.


Clustering Prediction Seismic source Earthquake Big data 


  1. 1.
    Scholz, C.H.: Large earthquake triggering, clustering, and the synchronization of faults. Bull. Seismol. Soc. Am. 100(3), 901–909 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Anderson, J.G., Nanjo, K.: distribution of earthquake cluster sizes in the western united states and in japan. Bull. Seismol. Soc. Am. 103(1), 412–423 (2013)CrossRefGoogle Scholar
  3. 3.
    Cornell, C.: Engineering seismic risk analysis. Bull. Seismol. Soc. Am. 58, 1583–1606 (1968)Google Scholar
  4. 4.
    Reiter, L.: Earthquake Hazard Analysis, Issues and Insights. Columbia University Press, New York (1990)Google Scholar
  5. 5.
    Anagnos, T., Kiremidjian, A.S.: A review of earthquake occurrence models for seismic hazard analysis. Probab. Eng. Mech. 3(1), 3–11 (1988)CrossRefGoogle Scholar
  6. 6.
    Hashemi, M., Alesheikh, A.A., Zolfaghari, M.R.: A spatio-temporal model for probabilistic seismic hazard zonation of Tehran. Comput. Geosci. 58, 8–18 (2013)CrossRefGoogle Scholar
  7. 7.
    Erdik, M., Biro, Y.A., Onur, T., Sesetyan, K., Birgoren, G.: Assessment of earthquake hazard in Turkey and neighboring regions. Ann. Geofis. 42(6), 1125–1138 (1999)Google Scholar
  8. 8.
    Erdik, M., Demircioglu, M., Sesetyan, K., Durukal, E., Siyahi, B.: Earthquake hazard in marmara region, turkey. Soil Dyn. Earthq. Eng. 24, 605–631 (2004)CrossRefGoogle Scholar
  9. 9.
    Zmazek, B., Todorovski, L., Džeroski, S., Vaupotič, J., Kobal, I.: Application of decision trees to the analysis of soil radon data for earthquake prediction. Appl. Radiat. Isot. 58(6), 697–706 (2003)CrossRefGoogle Scholar
  10. 10.
    Hashemi, M., Alesheikh, A.: Spatio-temporal analysis of Tehran’s historical earthquakes trends. In: Geertman, S., Reinhardt, W., Toppen, F. (eds.) Proceedings of Advancing Geoinformation Science for a Changing World, pp. 3–20. Springer, Utrecht, Netherlands (2011)CrossRefGoogle Scholar
  11. 11.
    Ledolter, J.: Data Mining and Business Analytics with R. Wiley, Hoboken (2013)CrossRefzbMATHGoogle Scholar
  12. 12.
    Conway, D., White, J.: Machine Learning for Hackers. O’Reilly Media, Sebastopol (2012)Google Scholar
  13. 13.
    Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  14. 14.
    United States Geological Survey (USGS) (2015). Retrieved from
  15. 15.
    United States Geological Survey (USGS) (2015). Retrieved from

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  1. 1.Geoinformatics Laboratory, School of Information SciencesUniversity of PittsburghPittsburghUSA

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