je XXXIX-B8, 2012 
\spect, Elevation, Soil 
acture; the ANN output 
nts of the occurrence of 
"e Qutput 
i’ 
Analysis result 
rt of LS analysis 
les 
and the equation (1), 
nput factors (quantitative 
0 
0 
  
0 
NN trainings would be 
mentioned, the qualitative 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
values were selected from 1,200 landslide records, and the 
summary of different frequencies every sample were 
considered as the output data. The number of neurons of hidden 
layer is more than twice the input data. The structure of Neural 
network is “9-18-17. The BP learning algorithm, with network 
training error of 10-6, and 500 times of training (Figure 2), was 
implemented to train various ANN architectures. 
(4.30 + 
£1.25 
0.20 
vi 
0. Sant 
t5 
0 77736 "ioo 150 200 250 300 350 400 450 500 
Training times 
Error 
  
  
Figure 2. Error and training times of ANN 
Some landslide test data were from worldwide authoritative 
website (United States Geological Survey (USGS), 
http://landslides.usgs.gov/recent; | EM-DAT International 
Disaster Database, http://www.em-dat.net/; International 
Consortium on Landslides Website (ICL), http://iclhq.org/). 
Gathering 100 landslide test data, the accuracy of the network 
is about 82.31%. The output data were considered to express 
the LS index values of pixels. The higher the value of LS, the 
more susceptible is that pixel to the occurrence frequencies of 
landslide. Based on the 1kmx1km grid, the LS map of China is 
then produced by ANN learning and multiplying the weighted 
factor layers (Figure 3). 
  
  
  
  
  
Figure 3. LS map of China based on multi-factors 
From the figure can clearly see that the southwest of China has 
higher susceptibility index than other areas, which also could 
be consistent with landslide reports from news. Meanwhile, the 
rainfall data from TRMM satellite can be used to predict the 
landslide area by overlaying the LS map. 
49 
5. CONCLUSIONS 
The most prominent features of landslide include information 
diversity, fuzziness, uncertainty and randomness, which bring 
great difficult for information processing. The processing 
ability of traditional ways and methods is limited, and may 
cause a lot of useful information losses, result in the degree of 
reliability reducing. With the strong nonlinear mapping 
capability, can simulate the possibility of the landslide and the 
relationship of complex factors (Chacón et al, 2006). 
A landslide susceptibility model has been developed for the 
whole country applying a scoring system with a set of relevant 
factors based on BP ANN, merging nonlinear elements by 
qualitative and quantitative indices. The result has been tested 
by the data of worldwide authoritative website, and the 
accuracy of the network is about 82.31% to meet landslide 
study requirements. The high susceptibility parts mainly in the 
southwest of China, characterized by the presence of 
landslide-prone sedimentary rocks, high seismicity, frequent 
severe earthquake and rainfall events and significant human 
activities in this area. 
The landslide susceptibility evaluation and mapping of China 
generated in this study constitutes a preliminary step for further 
more detailed susceptibility and hazard research, as well as a 
useful method for risk assessment, and provides theoretical 
basis for prediction and forecast of landslide disasters 
throughout China. 
Acknowledgements 
The work described in this paper was supported by National 
Basic Research Program of China (2012CB957702) and 
Innovation Program of Shanghai Municipal Education 
Commission(Project ID:10ZZ25) , and also supported by the 
Centre of Spatial Information Science and Sustainable 
Development, Tongji University. 
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