Abstract
Statistical landslide susceptibility mapping (LSM) models have been most widely used in literatures. However, limitations and uncertainties remain in these methods. The main goal of the current study was to test and compare the efficiency of a bivariate model (the weight of evidence (WoE)), a multivariate model (logistic regression (LR)) and a machine-learning algorithm (the support vector machine (SVM)) in LSM. Lushan County of China was chosen because of its mountainous terrain and high risky of devastating seismic activities. An inventory of 867 landslides was utilized in this study, 70% of which were used to train these models, and the rest 30% were used to validate their accuracies. Ten factors of aspect, elevation, slope, curvature, peak ground acceleration (PGA), distance to the river (DtoR), lithology, topographic wetness index (TWI), stream power index (SPI) and percentage of tree cover (PTC) were used as input of the landslide susceptibility mapping (LSM) models. Accuracy evaluation based on the areas under the receiver operating characteristic curves (AUC) showed that the LR model gives the highest success rate (78.2%) and prediction rate (76.4%), the SVM has the second-highest success rate (75.9%) and the WoE had the second-highest prediction rate (75.6%). Comparison results suggested that the LR and the SVM are proper models for LSM of the study area. The obtained susceptibility maps would benefit regional land planning and seismic landslide hazard mitigation in the study area.
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Abbreviations
- LSM:
-
Landslide susceptibility mapping
- LFZ:
-
Longmenshan fault zone
- SDF:
-
Shuangshi-Dachuan fault
- GIS:
-
Geographic information system
- DEM:
-
Digital elevation model
- TWI:
-
Topographic wetness index
- SPI:
-
Stream power index
- PGA:
-
Peak ground acceleration
- PGA:
-
Peak ground acceleration
- PTC:
-
Percentage of tree cover
- WoE:
-
Weight of evidence
- LR:
-
Logistic regression
- SVM:
-
Support vector machine
- ROC:
-
Receiver operating characteristic
- AUC:
-
Areas under ROC curves
- FR:
-
Frequency ratio
- IV:
-
Information value
- RBF:
-
Radial basis function
- CNN:
-
Convolutional neural network
- CAS:
-
Chinese Academy of Science
- LSI:
-
Landslide susceptibility index
- TP:
-
True positive
- TN:
-
True negative
- FP:
-
False positive
- FN:
-
False negative
- FPR:
-
False positive rate
- TPR:
-
True positive rate
- TOL:
-
Tolerances
- VIF:
-
Variance inflation variables
- MLR:
-
Maximum likelihood ratio
- LN:
-
Linear
- PL:
-
Polynomial
- SIG:
-
Sigmoid
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All financial supports were greatly acknowledged.
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This research was supported by the National Natural Science Foundation of China (51708199); Science and Technology Infrastructure Program of Guizhou Province (2020-4Y047; 2018-133-042); Fundamental Research Funds for the Central Universities (531118010069); Science and Technology Project of Transportation and Communication Ministry of Guizhou Province (2017-143-054); and Science and Technology Program of Beijing (Z181100003918005).
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Responsible Editor: Biswajeet Pradhan
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Zhou, S., Zhang, Y., Tan, X. et al. A comparative study of the bivariate, multivariate and machine-learning-based statistical models for landslide susceptibility mapping in a seismic-prone region in China. Arab J Geosci 14, 440 (2021). https://doi.org/10.1007/s12517-021-06630-5
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DOI: https://doi.org/10.1007/s12517-021-06630-5