Advertisement

Statistics Based Predictive Geo-spatial Data Mining: Forest Fire Hazardous Area Mapping Application

  • Jong Gyu Han
  • Keun Ho Ryu
  • Kwang Hoon Chi
  • Yeon Kwang Yeon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2642)

Abstract

In this paper, we propose two statistics based predictive geo-spatial data mining methods and apply them to predict the forest fire hazardous area. The proposed prediction models used in geo-spatial data mining are likelihood ratio and conditional probability methods. In these approaches, the prediction models and estimation procedures depend on the basic quantitative relationships of geo-spatial data sets relevant to the forest fire with respect to the selected areas of previous forest fire ignition. In order to make the prediction map for the forest fire hazardous area prediction map using the two proposed prediction methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. When the prediction power of the two proposed prediction models is compared, the likelihood ratio method is more powerful than the conditional probability method. The proposed model for prediction of the forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrences and effective placement of forest fire monitoring equipment and manpower.

Keywords

Forest Type Forest Fire Fire Ignition Likelihood Ratio Method Spatial Data Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    R.H. Guting. An Introduction to Spatial Database Systems. In Very Large Data Bases Jorunal(Publisher: SpringerVerlag), October 1994.Google Scholar
  2. 2.
    S. Shekhar and S. Chawla. Spatial Databases: Issues, Implementation and Trends. 2001.Google Scholar
  3. 3.
    G. Greenman. Turning a map into a cake layer of information. New York Times, Feb 12 2000.Google Scholar
  4. 4.
    K. Koperski, J. Adhikary, and J. Han. Spatial Data Mining: Progress and Challenges. In In Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD’96), 1996.Google Scholar
  5. 5.
    D. Mark. Geographical Information Science: Critical Issues in an Emerging Cross-Disciplinary Research Domain. NSF Workshop, Feb.Google Scholar
  6. 6.
    J. Neter and L. Wasseman. Applied Linear Statistical Models, 4th ed. IrwinGoogle Scholar
  7. 7.
    S. M. Weiss and C. A. Kulikowski. Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. Morgan Kaufman, 1991.Google Scholar
  8. 8.
    P. Kontkanen, P. Myllymaki, and H. Tirri. Predictive data mining with finite mixtures. Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD’96)Google Scholar
  9. 9.
    D. M. Skapura. Building Neural Networks. ACM Press, 1996.Google Scholar
  10. 10.
    J. R. Quinlan. Induction of decision trees. Machine Learning.Google Scholar
  11. 11.
    J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.Google Scholar
  12. 12.
    L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth International Group, 1984.Google Scholar
  13. 13.
    M. Mehta, R. Agrawal, and J. Rissanen. SLIQ: A fast scalable classifier for data mining. In Proc. 1996 Int. Conference on Extending Database Technology (EDBT’96), Avignon, France, March 1996.Google Scholar
  14. 14.
    J. Shafer, R. Agrawal, and M. Mehta. SPRINT: A scalable parallel classifier for data mining. In Proc. 1996 Int. Conf. Very Large Data Bases.Google Scholar
  15. 15.
    M. Kamber, L. Winstone, W. Gong, S. Cheng, and J. Han. Generalization and decision tree induction: Efficient classification in data mining. In Proc. of 1997. Int. Workshop on Research Issues on Data Engineering (RIDE’97)Google Scholar
  16. 16.
    Jong Gyu Han, Yeon Kwang Yeon, Kwang Hoon Chi and Keun Ho Ryu, Prediction of Forest Fire Hazardous Area Using Predictive Spatial Data Mining, Proc. of Int. Conf. on Information and Knowledge Engineering. P348–358, 2002Google Scholar
  17. 17.
    Lusted, L.B., Introduction to Medical Decision Making: Charles Thomas, Springfield, 271p.Google Scholar
  18. 18.
    Aspinall, P. J. and Hill, A.R., Clinical inferences and decisions-I. Diagnosis and Bayes’ theorem: Opthalmic and Physiological Optics, v. 3, p. 295–304, 1983.CrossRefGoogle Scholar
  19. 19.
    Spiegelhalter, D.J. and Knill-Jones, R.P., Statistical and knowledge-based approaches to clinical decision-support systems, with an application in gastroenterology: Journal of the royal Statistical Society, A, Part 1, p. 35–77, 1984Google Scholar
  20. 20.
    Aspinall, R.J., An inductive modeling procedure based on Bayes’ theorem for analysis of pattern in spatial data: Internationl Journal of Geographical Information System, v. 6(2), p. 105–121, 1992CrossRefGoogle Scholar
  21. 21.
    Reboh, R. and Reiter, J., A knowledge-based system for regional mineral resource assessment: Final report, SRI project 4119, p 267, 1983.Google Scholar
  22. 22.
    McCammon, R.B., Prospector II—The redesign of Prospector: AI system in Government, March 27–31, 1989, Washington, D.C., p. 88–92, 1989.Google Scholar
  23. 23.
    Reddy, R.K., Bonham-Carter, G.F. and Galley, A.G., Developing a geographic expert system for regional mapping of Volcanogenic Massive Sulphide (VMS) deposit potential: Nonrenewable Resources, v. 1(2), p. 112–124, 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jong Gyu Han
    • 1
  • Keun Ho Ryu
    • 2
  • Kwang Hoon Chi
    • 1
  • Yeon Kwang Yeon
    • 1
  1. 1.Korea Institute of Geosciences & Mineral ResourcesDaejeonRepublic of Korea
  2. 2.Chungbuk National UniversityCheongjuRepublic of Korea

Personalised recommendations