Application of Fuzzy Information Representation Using Frequency Ratio and Non-parametric Density Estimation to Multi-source Spatial Data Fusion for Landslide Hazard Mapping

  • Park No-Wook (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources) ;
  • Chi Kwang-Hoon (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources) ;
  • Kwon Byung-Doo (Department of Earth Science Education, Seoul National University)
  • Published : 2005.03.01

Abstract

Fuzzy information representation of multi-source spatial data is applied to landslide hazard mapping. Information representation based on frequency ratio and non-parametric density estimation is used to construct fuzzy membership functions. Of particular interest is the representation of continuous data for preventing loss of information. The non-parametric density estimation method applied here is a Parzen window estimation that can directly use continuous data without any categorization procedure. The effect of the new continuous data representation method on the final integrated result is evaluated by a validation procedure. To illustrate the proposed scheme, a case study from Jangheung, Korea for landslide hazard mapping is presented. Analysis of the results indicates that the proposed methodology considerably improves prediction capabilities, as compared with the case in traditional continuous data representation.

Keywords

References

  1. Agresti, A., 1990, Categorical data analysis. John Wiley & Sons, New York, 558 p
  2. An, P., Moon, W.M., and Rencz, A., 1991, Application of fuzzy set theory to integrated mineral exploration. Canadian Journal of Exploration Geophysics, 27, 1-11
  3. Bonham-Carter, G.F., 1994, Geographic information systems for geoscientists: modeling with GIS. Pergamon Press, New York, 398 p
  4. Bonham-Carter, G.F., Agterberg, F.P., and Wright, D.F., 1988, Integration of geological data set for gold exploration in Nova Scotia. Photogrammetric Engineering & Remote Sensing, 54, 1585-1592
  5. Carranza, E.J.M. and Hale, M., 2001, Geologically constrained fuzzy mapping of gold mineralization potential, Baguio district, Philippines. Natural Resources Research, 10, 125-136 https://doi.org/10.1023/A:1011500826411
  6. Chung, C.F. and Fabbri, A.G, 1993, The representation of geoscience information for data integration. Nonrenewable Resources, 2, 122-139 https://doi.org/10.1007/BF02272809
  7. Chung, C.F. and Fabbri, A.G., 1999, Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering & Remote Sensing, 65, 1389-1399
  8. Davis, J.C., 1986, Statistics and data analysis in geology. John Wiley & Sons, New York, 656 p
  9. Duda, R.O., Hart, P.E., and Stork, D.G., 2000, Pattern classification. John Wiley & Sons, New York, 654 p
  10. Fukunaga, K., 1990, Introduction to statistical pattern recognition. Academic Press, San Diego, 592 p
  11. Hastie, T., Tibshirani, R., and Friedman, J., 2001, The elements of statistical learning. Springer, New York, 533 p
  12. Kim, K.-S., 2001, Prediction of landslide probability by geomorphic characteristics and soil properties. KIGAM Bulletin, 5, 29-41
  13. Moon, W., 1990, Integration of geophysical and geological data using evidential belief function. IEEE Transactions on Geoscience and Remote Sensing, 28, 711-720 https://doi.org/10.1109/TGRS.1990.572988
  14. Moon, W., 1998, Integration and fusion of geological exploration data: a theoretical review of fuzzy logic approach. Geosciences Journal, 2, 175-183 https://doi.org/10.1007/BF02910163
  15. Park, N.-W., Chi, K.-H., Chung, C.F., and Kwon, B.-D., 2003a, GIS-based data-driven geological data integration using fuzzy logic: theory and application. Economic and Environmental Geology, 36, 243-255
  16. Park, N.-W., Chi, K.-H., Chung, C.F., and Kwon, B.-D., 2003b, Predictive spatial data fusion using fuzzy object representation and integration: application to landslide hazard assessment. Korean Journal of Remote Sensing, 19, 233-246
  17. Park, N.-W., Chi, K.-H., Lee, K.-J., and Kwon, B.-D., 2003c, Automatic estimation of threshold values for change detection of multi-temporal remote sensing images. Korean Journal of Remote Sensing, 19, 465-478
  18. Parzen, E., 1962, On the estimation of a probability density function and the mode, Annals of Mathematical Statistics, 33, 1065-1076 https://doi.org/10.1214/aoms/1177704472
  19. Silverman, B.W., 1986, Density estimation for statistics and data analysis. Chapman and Hall, Florida, 175 p
  20. SpatialModels Inc., 2004, Users guide of spatial prediction modeling system, 108 p
  21. Zadeh, L.A., 1965, Fuzzy sets. Information and Control, 8, 338-353 https://doi.org/10.1016/S0019-9958(65)90241-X
  22. Zimmermann, H.J., 1996, Fuzzy set theory and its applications. Kluwer Academic Publisher, Massachusetts, 435 p