Data Clustering and Zonationof Earthquake Building Damage Hazard Area Using FKCN and Kriging Algorithm

  • Edy IrwansyahEmail author
  • Sri Hartati
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)


The objective of this research is to construct the zonation of earthquake building damage hazard area using fuzzy kohonen clustering network (FKCN) algorithm for data clustering and kriging algorithm for data interpolation. Data used consists of the earth data in the form of peak ground acceleration (PGA), lithology and topographic zones and Iris plant database for algorithm validation. This research is comprised into three steps which are data normalization, data clustering and data interpolation using FKCN and kriging algorithm and the construction of zonation. Clusterization produces three classes of building damage hazard data. The first class is consisting of medium PGA,dominantby high compaction lithology in the topography of inland area. The second class with low PGA, dominant low compaction lithology in the lowland topographic zone and the third class with high PGA, dominant by un-very low compactionlithology in swamp topographic zone. Banda Aceh cityas location sample is divided into three building damage hazard zone which is high hazard zone, medium hazard zone and low hazard zone for building damage which is located towards inland area.


Clusterization damage hazard earthquake FKCN algorithm 


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  1. 1.
    Almeida, C.W.D., Souza, R.M.C.R., Ana Lúcia, B.: IFKCN: Applying Fuzzy Kohonen Clustering Network to Interval Data. In: WCCI 2012 IEEE World Congress on Computational Intelligence, pp. 1–6 (2012)Google Scholar
  2. 2.
    Bezdek, J.C., Tsao, E.C.-K., Pal, N.R.: Fuzzy Kohonen Clustering Networks. Fuzzy Systems 27(5), 757–764 (1992)Google Scholar
  3. 3.
    Carreño, M.L., Cardona, O.D., Barbat, A.H.: Computational Tool for Post-Earthquake Evaluation of Damage in Buildings. J. Earthquake Spectra 26(1), 63–86 (2010)CrossRefGoogle Scholar
  4. 4.
    Elenas, A., Vrochidou, E., Alvanitopoulos, P., Ioannis Andreadis, L.: Classification of Seismic Damages in Buildings Using Fuzzy Logic Procedures. In: Papadrakakis, et al. (eds.) Computational Methods in Stochastic Dynamic. Computational Methods in Applied Sciences, vol. 26, pp. 335–344. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Fallahian, S., Seyedpoor, S.M.: A Two Stage Method for Structural Damage Identification Using An Adaptive Neuro-Fuzzy Inference System and Particle Swarm Optimization. Asian J. of Civil Engineering (Building and Housing) 11(6), 795–808 (2010)Google Scholar
  6. 6.
    Fisher, M.M., Getis, A. (eds.): Handbook of Applied Spatial Analysis –Software Tools, Methods and Applications. Springer, Heidelberg (2010)Google Scholar
  7. 7.
    Han, J., Kamber, M., Pei, J.: Data Mining Concept and Techniques, 3rd edn. Morgan Kaufmann-Elsevier, Amsterdam (2012)Google Scholar
  8. 8.
    Hathaway, R.J., Bezdek, J.C.: Nerf C-Means Non-Euclidean Relation Fuzzy Clustering. Pattern Recognition 27(3), 429–437 (1994)CrossRefGoogle Scholar
  9. 9.
    Irwansyah, E.: Building Damage Assessment Using Remote Sensing, Aerial Photograph and GIS Data-Case Study in Banda Aceh after Sumatera Earthquake 2004. In: Proceeding of Seminar on Intelligent Technology and Its Application (SITIA 2010), vol. 11(1), pp. 57–65 (2010)Google Scholar
  10. 10.
    Irwansyah, E., Hartati, S.: Zonasi Daerah BahayaKerusakanBangunanAkibatGempaMenggunakanAlgoritma SOM Dan AlgoritmaKriging. In: Proceeding of Seminar Nasional TeknologiInformasi (SNATI 2012), vol. 9(1), pp. 26–33 (2012) (in Bahasa)Google Scholar
  11. 11.
    Irwansyah, E., Winarko, E., Rasjid, Z.E., Bekti, R.D.: Earthquake Hazard Zonation Using Peak Ground Acceleration (PGA) Approach. Journal of Physics: Conference Series 423(1), 1–9 (2013)Google Scholar
  12. 12.
    Jabbar, N., Ahson, S.I., Mehrotra, M.: Fuzzy Kohonen Clustering Network for Color Image Segmentation. In: 2009 International Conference on Machine Learning and Computing, Australia, vol. 3, pp. 254–257 (2011)Google Scholar
  13. 13.
    Jiang, S.F., Zhang, C.M., Zhang, S.: Two-Stage Structural Damage Detection Using Fuzzy Neural Networks and Data Fusion Techniques. Expert Systems with Application 38(1), 511–519 (2011)CrossRefGoogle Scholar
  14. 14.
    Kohonen, T.: New Developments and Applications of Self-Organizing Map. In: Proceeding of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP 1996) (1996)Google Scholar
  15. 15.
    Lind, C.T., George Lee, C.S.: Neural Fuzzy System: A Neuro-Fuzzy Synergism to Intelligent System. Prentice-Hall, London (1996)Google Scholar
  16. 16.
    Mittal, A., Sharma, S., Kanungo, D.P.: A Comparison of ANFIS and ANN for the Prediction of Peak Ground Acceleration in Indian Himalayan Region. In: Deep, K., Nagar, A., Pant, M., Bansal, J.C. (eds.) Proceedings of the International Conf. on SocProS 2011. AISC, vol. 131, pp. 485–495. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    Miura, H., Wijeyewickrema, A.C., Inoue, S.: Evaluation of Tsunami Damage in the Eastern Part of Sri Lanka Due To the 2004 Sumatra Earthquake Using High-Resolution Satellite Images. In: Proceedings of 3rd International Workshop on Remote Sensing for Post-Disaster Response, pp. 12–13 (2005)Google Scholar
  18. 18.
    Ponnusamy, J.: GIS based Earthquake Risk-Vulnerability Analysis and Post-quake Relief. In: Proceedings of 13th Annual International Conference and Exhibition on Geospatial Information Technology and Application (MapIndia), Gurgaon, India (2010)Google Scholar
  19. 19.
    Sanchez-Silva, M., Garcia, L.: Earthquake Damage Assessment Based on Fuzzy Logic and Neural Networks. Earthquake Spectra 17(1), 89–112 (2001)CrossRefGoogle Scholar
  20. 20.
    Slob, S., Hack, R., Scarpas, T., van Bemmelen, B., Duque, A.: A Methodology for Seismic Microzonation Using GIS And SHAKE—A Case Study From Armenia, Colombia. In: Proceedings of 9th Congress of the International Association for Engineering Geology and the Environment: Engineering Geology for Developing Countries, pp. 2843–2852 (2002)Google Scholar
  21. 21.
    United States Geological Survey-USGS,
  22. 22.
    Yang, Y., Jia, Z., Chang, C., Qin, X., Li, T., Wang, H., Zhao, J.: An Efficient Fuzzy Kohonen Clustering Network Algorithm. In: Proceedings of 5th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 510–513. IEEE Press (2008)Google Scholar

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceBina Nusantara UniversityJakartaIndonesia
  2. 2.Deptartment of Computer Science and ElectronicUniversitas Gadjah MadaYogyakartaIndonesia

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