Abstract
Landslides in mountain settlements are among the most widespread and dangerous geohazards. In this study, we aimed to assess landslide susceptibility using Wenchuan, southwest China, as a case. For this purpose, we constructed an optimization method that combines a convolutional neural network with the machine learning algorithm of support vector machines, quadratic discriminant analysis, Bayesian optimized gradient boosting tree, and Bayesian optimized random forest. The model inputs were 13,886 historical seismic-induced landslide events interpreted from remote sensing imagery and ten evaluation features: elevation, slope angle, slope aspect, plan curvature, profile curvature, distance to roads, distance to rivers, distance to faults, land use pattern, and soil texture. The output was the probability of landslide occurrence for each prediction unit. Finally, we evaluated the assessed outcomes using both the receiver operating characteristic curve and 1074 latest recorded landslide dataset (2013–2020). The calculations showed that the overall susceptibility values to landslides in the high–very high interval produced by the hybrid convolutional neural networks was 9.95%–16.91%, which is close to the actual landslide susceptibility of the region. The receiver operating characteristic curve and statistical analysis of the latest landslide event outcomes demonstrated that the hybrid Bayesian optimized gradient boosting tree model had a higher classification accuracy than the other classifiers presented in this study. The research findings are available to local governments and disaster management authorities in guiding disaster prevention, mitigation policy formulation, and land use and provide reference value for evaluating landslide susceptibility in other mountainous areas.
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Abbreviations
- AUC:
-
Area under the receiver operating characteristic curve
- BO_GBT:
-
Bayesian optimized gradient boosting tree
- BO_RF:
-
Bayesian optimized random forest
- CNN:
-
Convolutional neural network
- CSLC:
-
Classic supervised learning classifiers
- DF:
-
Distance to faults
- DRI:
-
Distance to rivers
- DRO:
-
Distance to roads
- ELE:
-
Elevation
- LCF:
-
Landslide conditioning factors
- LSM:
-
Landslide susceptibility mapping
- LSV:
-
Landslide susceptibility value
- LUP:
-
Land use pattern
- ML:
-
Machine learning
- PLC:
-
Plan curvature
- PRC:
-
Profile curvature
- QDA:
-
Quadratic discriminant analysis
- ROC:
-
Receiver operating characteristic curve
- SAN:
-
Slope angle
- SLA:
-
Slope aspect
- ST:
-
Soil texture
- SVM:
-
Support vector machines
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Acknowledgements
The authors appreciate the support from Peng Du on the landslide data and Jintao Huang on the algorithm discussion and acknowledge the insightful comments of the editor and anonymous referees that were very helpful for an improvement in an earlier version of the manuscript.
Funding
This research was supported by the National Natural Science Foundation of China under Grant (No. 41871174) and the project of Science and Technology Department of Sichuan Province under Grant (No. 2020YFSY0013).
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Gao, Z., Ding, M. Application of convolutional neural network fused with machine learning modeling framework for geospatial comparative analysis of landslide susceptibility. Nat Hazards 113, 833–858 (2022). https://doi.org/10.1007/s11069-022-05326-7
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DOI: https://doi.org/10.1007/s11069-022-05326-7