Enhanced Landslide Susceptibility Assessment in Western Sichuan Utilizing DCGAN-Generated Samples
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Inventory of Landslides
2.3. Landslide Factors
3. Methods
3.1. VIF Analysis and GeoDetector
3.2. Data Augmentation Based on DCGAN
3.3. LSA Machine Learning Models
3.3.1. Support Vector Machine (SVM)
3.3.2. Convolutional Neural Network (CNN)
3.3.3. Residual Neural Network (ResNet)
3.4. Evaluation Indicators
4. Results
4.1. Landslide Factor Screening
4.2. Quality Analysis of Additional Landslide Samples
- (1)
- When the number of additional samples is 100, 200, and 300, the testing accuracies of the SVM are improved by 2.56%, 2.66%, and 2.3%, respectively. However, those of the CNN and ResNet show no significant changes, and there is a difference of approximately 5% to 10% between the accuracy of the training and testing datasets, indicating the presence of overfitting.
- (2)
- With 400 and 500 additional samples, the testing accuracies of the SVM are improved by 1.88% and 2.18%, respectively. The testing accuracies of the CNN are improved by 1.54% and 2.57%, respectively. Moreover, those of the ResNet are improved by 3.37% (400) and 5.28%, respectively.
- (3)
- When there are 600 additional samples, compared to 200 additional samples, the testing accuracy of the SVM is decreased by 2.31%. However, it is improved by 0.35% compared to the original dataset. Additionally, compared to 500 additional samples, the testing accuracies of the CNN and ResNet were decreased by 2.11% and 4.3%, respectively, although compared to the original dataset, they were improved by 0.46% and 0.98%, respectively.
4.3. LSA Results
4.3.1. Effectiveness of LSA Machine Learning Models
4.3.2. Analysis of Landslide Susceptibility Mapping (LSM)
4.3.3. Validation Based on Large-Scale Landslide Images
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Name | Sources | Type | Resolution |
---|---|---|---|
Elevation | NASA SRTM DEM | raster | 30 (m) |
Geological faults | Active tectonic map of China (1:4,000,000) | vector | 1:4,000,000 |
Road | National Basic Geographic Database | vector | 1:250,000 |
River system | National Basic Geographic Database | vector | 1:250,000 |
Lithology | National 1:200,000 digital geological map | vector | 1:200,000 |
Landform | National 1:200,000 digital geological map | vector | 1:200,000 |
Soil | 1:1 million soil map of the People’s Republic of China | vector | 1:1,000,000 |
Precipitation | China Meteorological Administration (CMA) | station | - |
Land use | GLOBELAND30 | raster | 30 (m) |
NDVI | Google Earth Engine | raster | 30 (m) |
Landslide Factors | Value | Landslides | Pixel Number | Frequency Ratios |
---|---|---|---|---|
Elevation (m) | <1500 | 762 | 12,521,106 | 10.701 |
1500~2100 | 566 | 22,803,722 | 4.364 | |
2100~2700 | 378 | 32,203,639 | 2.064 | |
2300~3200 | 152 | 31,160,160 | 0.858 | |
3200~3600 | 66 | 43,476,058 | 0.267 | |
3600~4000 | 51 | 59,416,050 | 0.151 | |
4000~4300 | 14 | 54,487,458 | 0.045 | |
4300~4600 | 8 | 58,221,426 | 0.024 | |
>4600 | 3 | 37,378,450 | 0.014 | |
NDVI | <0.1 | 26 | 11,539,489 | 0.396 |
0.1~0.3 | 147 | 36,268,491 | 0.713 | |
0.3~0.5 | 788 | 159,049,040 | 0.871 | |
0.5~0.7 | 886 | 112,372,150 | 1.386 | |
>0.7 | 153 | 32,438,852 | 0.829 | |
Precipitation (mm) | <550 | 155 | 81,219,497 | 0.336 |
550~650 | 256 | 93,794,711 | 0.480 | |
650~750 | 490 | 106,941,315 | 0.806 | |
750~1000 | 798 | 65,695,362 | 2.136 | |
>1000 | 301 | 4,017,184 | 13.175 | |
Distance to a river (km) | <1 | 519 | 29232080 | 3.122 |
1~3 | 585 | 56,067,215 | 1.835 | |
3~6.3 | 402 | 85,449,923 | 0.827 | |
6.3~9.6 | 220 | 70,887,607 | 0.546 | |
9.6~13 | 143 | 52,398,139 | 0.480 | |
13~17 | 80 | 34,924,928 | 0.403 | |
>17 | 51 | 22,708,218 | 0.395 | |
Lithology | A (Clastic rock) | 724 | 107,987,222 | 1.179 |
B (Granite) | 34 | 6,525,398 | 0.916 | |
C (Metamorphic rock) | 269 | 133,115,327 | 0.355 | |
D (Continental deposit) | 317 | 49,941,742 | 1.116 | |
E (Urban) | 239 | 9,216,852 | 4.560 | |
F (Lake) | 3 | 111,410 | 4.735 | |
G (Carbonatite) | 251 | 31,535,669 | 1.400 | |
H (Outcrop) | 0 | 660,414 | 0 | |
I (Basalt) | 163 | 12,401,449 | 2.311 | |
Distance to road (km) | <0.5 | 964 | 32,899,167 | 5.152 |
0.5~1.5 | 458 | 47,514,033 | 1.695 | |
1.5~3.5 | 314 | 71,330,026 | 0.774 | |
3.5~6 | 136 | 61,810,401 | 0.387 | |
6~10 | 63 | 58,396,257 | 0.190 | |
10~15 | 31 | 36,685,427 | 0.149 | |
>15 | 34 | 43,032,799 | 0.139 |
Landslide Factors | VIF | TOL |
---|---|---|
Aspect | 1.00676 | 0.99329 |
Lithology | 1.09788 | 0.91085 |
Profile curvature | 1.1011 | 0.90818 |
Plan curvature | 1.22913 | 0.81358 |
Distance to fault | 1.28592 | 0.77765 |
Landform | 1.35468 | 0.73818 |
NDVI | 1.41616 | 0.70613 |
Land use | 1.45153 | 0.68892 |
Distance to a river | 1.45996 | 0.68495 |
Soil | 1.8564 | 0.53868 |
Relief amplitude | 1.99312 | 0.50173 |
Precipitation | 2.34496 | 0.42645 |
Distance to road | 2.64131 | 0.37860 |
SPI | 4.15778 | 0.24051 |
TWI | 4.18707 | 0.23883 |
Elevation | 5.88457 | 0.16994 |
STI | 9.73319 | 0.10274 |
Slope | 11.0813 | 0.09024 |
Hyperparameter | DCGAN | CNN | ResNet18 |
---|---|---|---|
Kernel | 4 × 4 | 3 × 3 | 3 × 3 |
Pooling | - | 2 × 2 | 3 × 3 |
Activation Function | D: LeakyReLU G: Tanh,ReLu | Tanh | ReLU |
Optimizer | Adam | Adam | Adam |
Loss Function | BCELoss | CELoss | CELoss |
Learning Rate | D: 0.0002 G: 0.001 | 5 × 10−4 | 5 × 10−4 |
Epoch | 6000 | 150 | 150 |
Model | ACC% | PRE | TPR | F1-Socer | MCC |
---|---|---|---|---|---|
SVM | 77.08 | 0.7627 | 0.7607 | 0.7604 | 0.5236 |
CNN | 86.69 | 0.8631 | 0.8731 | 0.8681 | 0.7339 |
ResNet | 86.50 | 0.8718 | 0.8401 | 0.8557 | 0.7294 |
DCGAN-SVM | 79.26 | 0.7927 | 0.7906 | 0.7826 | 0.5853 |
DCGAN-CNN | 89.26 | 0.9074 | 0.8741 | 0.8899 | 0.7856 |
DCGAN-ResNet | 91.78 | 0.9149 | 0.9094 | 0.9121 | 0.8257 |
Model | Very Low | Low | Moderate | High | Very High |
---|---|---|---|---|---|
CNN | 0 | 0 | 12.24% | 28.68% | 59.08% |
ResNet | 0 | 0 | 19.62% | 60.42% | 19.96% |
DCGAN-CNN | 0 | 0 | 16.37% | 22.65% | 60.98% |
DCGAN-ResNet | 0 | 4.15% | 5.79% | 24.79% | 66.5% |
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Tong, Y.; Luo, H.; Qin, Z.; Xia, H.; Zhou, X. Enhanced Landslide Susceptibility Assessment in Western Sichuan Utilizing DCGAN-Generated Samples. Land 2025, 14, 34. https://doi.org/10.3390/land14010034
Tong Y, Luo H, Qin Z, Xia H, Zhou X. Enhanced Landslide Susceptibility Assessment in Western Sichuan Utilizing DCGAN-Generated Samples. Land. 2025; 14(1):34. https://doi.org/10.3390/land14010034
Chicago/Turabian StyleTong, Yuanxin, Hongxia Luo, Zili Qin, Hua Xia, and Xinyao Zhou. 2025. "Enhanced Landslide Susceptibility Assessment in Western Sichuan Utilizing DCGAN-Generated Samples" Land 14, no. 1: 34. https://doi.org/10.3390/land14010034
APA StyleTong, Y., Luo, H., Qin, Z., Xia, H., & Zhou, X. (2025). Enhanced Landslide Susceptibility Assessment in Western Sichuan Utilizing DCGAN-Generated Samples. Land, 14(1), 34. https://doi.org/10.3390/land14010034