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IJMLC 2014 Vol. 4(6): 547-552 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V6.471

Determination of Topographic Factors to Initiate Debris Flow Using Statistical Analysis

Chien-Yuan Chen, Ho-Wen Chen, and Zhao-Jun Chen

Abstract—This study used GIS to determine 19 topographic indexes, four geologic indexes, and two rainfall data derived indexes of the Laonun River Basin, in southern Taiwan. The four topographic factors, including the effective area of basin, elongation ratio of basin, relief energy, and relief volume, were selected among the 19 topographic indexes using the SPSS multi-variable statistical analysis and the principal components analysis. The four topographic factors combine the four geologic factors and two rainfall factors and were selected for estimating debris flow prone creeks. The ten factors were further applied by the Fisher's Discriminant Analysis and Logistic Regression Analysis to evaluate the potentials of debris flow prone creeks in the basin. There were 13 sub-basins initiated debris flows and 41 non-debris flow sub-basins when Typhoon Morakot hit Taiwan in 2009. The validated results show that the correctness of Fisher’s model for the samples is 81.48 % and 92.6 % via the Logistic Regression model. Both models showed acceptable accuracy, and the Logistic model had better accuracy herein. The Logistic Regression Analysis was adapted to evaluate the potential of debris flow sub-basins to assist in developing risk management in the basin.

Index Terms—Debris flow, GIS, principal components analysis, linear regression analysis.

Chien-Yuan Chen and Zhao-Jun Chen are with the Civil and Water Resources Engineering Department, National Chiayi University, Chiayi City 60004, Taiwan R.O.C. (e-mail: chienyuc@mail.ncyu.edu.tw, tainan946012@yahoo.com.tw).
Ho-Wen Chen is with the Department of Environmental Science and Engineering, Tunghai University, Taichung City 40704, Taiwan R.O.C. (e-mail: hwchen@thu.edu.tw).


Cite: Chien-Yuan Chen, Ho-Wen Chen, and Zhao-Jun Chen, "Determination of Topographic Factors to Initiate Debris Flow Using Statistical Analysis," International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 547-552, 2014.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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