The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 865–869, 2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 865–869, 2015

  29 Apr 2015

29 Apr 2015

Analysis of landslide hazard area in Ludian earthquake based on Random Forests

J.-C. Xie1, R. Liu1, H.-W. Li1, and Z.-L. Lai2 J.-C. Xie et al.
  • 1College Of Geophysics,Chengdu University of Technology,Chengdu 610059,China
  • 2College of Earth Sciences,Chengdu University of Technology,Chengdu 610059,China

Keywords: Landslide; Random Forest; Evaluation factors; Generalization error; ROC graphs

Abstract. With the development of machine learning theory, more and more algorithms are evaluated for seismic landslides. After the Ludian earthquake, the research team combine with the special geological structure in Ludian area and the seismic filed exploration results, selecting SLOPE(PODU); River distance(HL); Fault distance(DC); Seismic Intensity(LD) and Digital Elevation Model(DEM), the normalized difference vegetation index(NDVI) which based on remote sensing images as evaluation factors. But the relationships among these factors are fuzzy, there also exists heavy noise and high-dimensional, we introduce the random forest algorithm to tolerate these difficulties and get the evaluation result of Ludian landslide areas, in order to verify the accuracy of the result, using the ROC graphs for the result evaluation standard, AUC covers an area of 0.918, meanwhile, the random forest’s generalization error rate decreases with the increase of the classification tree to the ideal 0.08 by using Out Of Bag(OOB) Estimation. Studying the final landslides inversion results, paper comes to a statistical conclusion that near 80% of the whole landslides and dilapidations are in areas with high susceptibility and moderate susceptibility, showing the forecast results are reasonable and adopted.