landslide susceptibility mapping on a global scale using the method of logistic regression
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2017
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Abstract
This paper proposes a statistical model for mapping global
landslide susceptibility based on logistic regression. After investigating
explanatory factors for landslides in the existing literature, five factors
were selected for model landslide susceptibility: relative relief, extreme
precipitation, lithology, ground motion and soil moisture. When building the
model, 70 % of landslide and nonlandslide points were randomly selected
for logistic regression, and the others were used for model validation. To
evaluate the accuracy of predictive models, this paper adopts several
criteria including a receiver operating characteristic (ROC) curve method.
Logistic regression experiments found all five factors to be significant in
explaining landslide occurrence on a global scale. During the modeling process,
percentage correct in confusion matrix of landslide classification was
approximately 80 % and the area under the curve (AUC) was nearly 0.87.
During the validation process, the above statistics were about 81 % and
0.88, respectively. Such a result indicates that the model has strong
robustness and stable performance. This model found that at a global scale,
soil moisture can be dominant in the occurrence of landslides and topographic
factor may be secondary.
| Reference Key |
lin2017naturallandslide
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|---|---|
| Authors | ;L. Lin;L. Lin;Q. Lin;Q. Lin;Y. Wang;Y. Wang |
| Journal | anziam journal |
| Year | 2017 |
| DOI |
10.5194/nhess-17-1411-2017
|
| URL | |
| Keywords |
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