Landslide Susceptibility Assesment Based on Optimized Random Forest Model

LIU Jian; LI Shulin; CHEN Tao

doi:10.13203/j.whugis20160515

Volume 43 Issue 7

Jul. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(7): 1085-1091. > DOI: 10.13203/j.whugis20160515

LIU Jian, LI Shulin, CHEN Tao. Landslide Susceptibility Assesment Based on Optimized Random Forest Model[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 1085-1091. DOI: 10.13203/j.whugis20160515

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Landslide Susceptibility Assesment Based on Optimized Random Forest Model

LIU Jian^{1, 2,},
LI Shulin^1, ,,
CHEN Tao¹

1.
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
2.
Key Laboratory of Earthquake Geodesy, Institute of Seismology, CEA, Wuhan 430071, China

Funds:

The National High Technology Research and Development Program of China(863 Program) 2012AA121303

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Author Bio:
LIU Jian, PhD candidate, engineer, specializes in cloud computing and geological disaster assessment. E-mail:linefanliu@163.com
Corresponding author:
LI Shulin, postgraduate. E-mail: lishulincug@gmail.com
Received Date: October 18, 2017
Published Date: July 04, 2018

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Abstract

Abstract

The research area is located in Shazhenxi town and Xietan town of Three Gorges reservoir area in this paper. In order to obtain better results that discrete the continuous factors of landslide, entropy based on minimal description length principle(Ent-MDLP) method is used. To avoid the influence of correlation between factors, we calculate the Pearson correlation coefficient to remove high correlation factor. In order to obtain more accurate non-landslide sample points, the non-landslide sample points are randomly selected from the very low and low susceptible regions predicted by the entropy method. For the optimized random forests model, the optimal random features and its number are determined by iterative calculation of out-of-bag error estimation. Then the optimized random forest is evaluated for the landslide of the study area, and the landslide susceptibility level is divided. The model is compared with the methods of logistic regression, support vector machine and non-optimized random forest. The accuracy of each model is evaluated by plotting the receiver sensitivity curve of each algorithm. The optimized random forest's area is the highest, which the area under the curve is 91.8%. These show that the random forest model is optimized with more high-predictive power in landslide-prone assessment.
- Three Gorges reservoir area,
- landslide,
- discretization,
- random forest,
- optimization,
- susceptibility assessment

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References

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