DATA MINING METHODS FOR GIS ANALYSIS OF SEISMIC VULNERABILITY

Florin Leon, Gabriela M. Atanasiu

2006

Abstract

This paper aims at designing some data mining methods of evaluating the seismic vulnerability of regions in the built infrastructure. A supervised clustering methodology is employed, based on k-nearest neighbor graphs. Unlike other classification algorithms, the method has the advantage of taking into account any distribution of training instances and also data topology. For the particular problem of seismic vulnerability analysis using a Geographic Information System, the gradual formation of clusters (for different values of k) allows a decision- making stakeholder to visualize more clearly the details of the cluster areas. The performance of the k-nearest neighbor graph method is tested on three classification problems, and finally it is applied to a sample from a digital map of Iaşi, a large city located in the North-Eastern part of Romania.

References

  1. Atanasiu, G. M., Leon, F., 2006. Spatial Infrastructure Information (SII) Based Management for Seismic Vulnerability of Built Urban Fund. Research Grant 3202 Report, CEEX Program.
  2. Eick, C. F., Zeidat, N., Zhao, Z., 2004. Supervised Clustering - Algorithms and Benefits. In Proc. International Conference on Tools with AI (ICTAI), Boca Raton, Florida, pp. 774-776.
  3. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., 1996. From Data Mining to KnowledgeDiscovery: An Overview. In Advances in Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, pp. 1 - 34.
  4. Han, J. Kamber, M., 2000. Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers.
  5. Lavakare, A., Krovvidi, A., 2001. GIS & Mapping for Seismic Risk Assessment. National seminar on Habitat Safety against Earthquakes and Cyclones, New Delhi.
  6. Martin, B., 1995. Instance-Based Learning: Nearest Neighbour with Generalisation, Master of Science Thesis, University of Waikato, Hamilton, New Zealand.
  7. Mitchell, T.M., 1997. Machine Learning, McGraw Hill.
  8. National Institute for Building Sciences, 2001. Earthquake loss estimation methodology HAZUS99 SR2, Technical manuals I-III National Institute for Building Sciences, Washington, DC.
  9. Nilsson, N. J., 1996. Introduction to Machine Learning. Stanford University, http://ai.stanford.edu/people/ nilsson/mlbook.html.
  10. Norton, T.R., Abdullah, M.M., 2004. Combined Hurricane and Earthquake Hazard Component Vulnerability Analysis. 2004 ANCER Annual Meeting: Networking of Young Earthquake Engineering Researchers and Professionals, Honolulu, Hawaii.
  11. Simpson, D. M., Rockaway, T. D., Weigel, T. A., Coomes, P. A., Holloman, C. O., 2005. Framing a new approach to critical infrastructure modelling and extreme events. International Journal of Critical Infrastructure Systems, Vol. 1, Nos. 2/3.
  12. Tan, P.N., Steinbach, M., Kumar, V., 2005. Introduction to Data Mining. Addison Wesley.
  13. Witten, I. H., Frank, E., 2000. Data Mining: Practical machine learning tools with Java implementations, Morgan Kaufmann, San Francisco.
Download


Paper Citation


in Harvard Style

Leon F. and M. Atanasiu G. (2006). DATA MINING METHODS FOR GIS ANALYSIS OF SEISMIC VULNERABILITY . In Proceedings of the First International Conference on Software and Data Technologies - Volume 2: ICSOFT, ISBN 978-972-8865-69-6, pages 153-156. DOI: 10.5220/0001308301530156


in Bibtex Style

@conference{icsoft06,
author={Florin Leon and Gabriela M. Atanasiu},
title={DATA MINING METHODS FOR GIS ANALYSIS OF SEISMIC VULNERABILITY},
booktitle={Proceedings of the First International Conference on Software and Data Technologies - Volume 2: ICSOFT,},
year={2006},
pages={153-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001308301530156},
isbn={978-972-8865-69-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Software and Data Technologies - Volume 2: ICSOFT,
TI - DATA MINING METHODS FOR GIS ANALYSIS OF SEISMIC VULNERABILITY
SN - 978-972-8865-69-6
AU - Leon F.
AU - M. Atanasiu G.
PY - 2006
SP - 153
EP - 156
DO - 10.5220/0001308301530156