Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Description of the Study Area
2.2. Landslide Inventory Mapping
2.3. Landslide Conditioning Factors
2.3.1. Topographic Factors
2.3.2. Geological Factors
2.3.3. Land Use and Land Cover Factors
2.3.4. Hydrological Factors
2.3.5. Geophysical Factor
3. Methodology
3.1. Conditioning Factors Analysis
3.1.1. Multicollinearity Analysis
3.1.2. Frequency Ratio Method
3.2. Convolutional Neural Networks
3.3. Proposed Model
3.4. Evaluation and Comparison Methods
3.5. Sensitivity Analysis of Conditioning Factors
4. Results
4.1. Selection and Analysis of the Landslide Conditioning Factors
4.2. Construction of Proposed Model
4.3. Generation of Landslide Susceptibility Maps
4.4. Evaluation and Comparison of Results
4.5. Sensitivity Analysis Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Conditioning Factors | Collinearity Statistics | |
---|---|---|
Tolerance | VIF | |
Aspect | 0.967 | 1.034 |
Curvature | 0.892 | 1.121 |
Elevation | 0.548 | 1.823 |
Distance to faults | 0.92 | 1.087 |
Land use | 0.787 | 1.271 |
Lithology | 0.829 | 1.206 |
NDVI | 0.741 | 1.35 |
PGA | 0.806 | 1.241 |
Relief | 0.32 | 3.124 |
Distance to rivers | 0.566 | 1.766 |
Distance to roads | 0.74 | 1.352 |
Slope | 0.287 | 3.488 |
SPI | 0.733 | 1.365 |
TWI | 0.584 | 1.712 |
No. | Parameters | Values |
---|---|---|
1 | Conventional kernel size (1D) | (3, 3) |
2 | Conventional kernel size (2D) | (3, 3) |
3 | Pooling size (2D) | (2, 2) |
4 | Loss function | Cross entropy |
5 | Optimizer | Adagrad |
6 | Epoch | 300 |
7 | Batch size | 32 |
8 | Learning rate | 0.08 |
9 | Activation function | ReLU |
Classes | Hybrid | CNN-2D | CNN-1D | RF | SVM |
---|---|---|---|---|---|
Very low | 611.7 (38.1%) | 552.81 (34.5%) | 741.69 (46.2%) | 294 (18.3%) | 377.93 (22.6%) |
Low | 102.69 (6.4%) | 176.45 (11.0%) | 35.99 (2.2%) | 356.09 (22.2%) | 293.84 (18.3%) |
Moderate | 80.43 (5.0%) | 145.57 (9.1%) | 23.37 (1.5%) | 338.2 (21.1%) | 340.86 (21.3%) |
High | 101.91 (6.3%) | 172.21 (10.7%) | 78.34 (4.9%) | 320.72 (20.0%) | 313.56 (19.6%) |
Very high | 706.95 (44.1%) | 556.62 (34.7%) | 724.29 (45.2%) | 294.66 (18.4%) | 227.49 (17.3%) |
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Yang, X.; Liu, R.; Yang, M.; Chen, J.; Liu, T.; Yang, Y.; Chen, W.; Wang, Y. Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping. Remote Sens. 2021, 13, 2166. https://doi.org/10.3390/rs13112166
Yang X, Liu R, Yang M, Chen J, Liu T, Yang Y, Chen W, Wang Y. Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping. Remote Sensing. 2021; 13(11):2166. https://doi.org/10.3390/rs13112166
Chicago/Turabian StyleYang, Xin, Rui Liu, Mei Yang, Jingjue Chen, Tianqiang Liu, Yuantao Yang, Wei Chen, and Yuting Wang. 2021. "Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping" Remote Sensing 13, no. 11: 2166. https://doi.org/10.3390/rs13112166