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Advances in Civil Engineering
/
2021
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Article
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Tab 5
/
Research Article
GIS-Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China
Table 5
Statistical results of landslide susceptibility mapping.
Models
Susceptibility
Class area (km
2
)
Class ratio (%)
Landslides count
Landslides ratio (%)
IM
Very low
259.95
17.24
1
1
Low
492.91
32.69
3
3
Moderate
407.12
27.00
13
13
High
229.04
15.19
29
29
Very high
118.82
7.88
54
54
FR
Very low
471.95
31.30
0
0
Low
529.86
35.14
2
2
Moderate
309.71
20.54
14
14
High
130.13
8.63
47
47
Very high
66.19
4.39
37
37
ANN
Very low
215.95
14.32
0
0
Low
460.46
30.54
1
1
Moderate
442.66
29.36
4
4
High
276.77
18.36
30
30
Very high
112.00
7.45
65
65