Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China
Abstract
1. Introduction
2. Study Area
3. Data and Methods
3.1. Debris Flow Inventory
3.2. Susceptibility Assessment Process
3.3. Sample Data Preparation
3.4. Conditioning Factors
- (1)
- Topography: This includes elevation, slope, aspect, topographic wetness index, stream power index, topographic relief, drainage basin shape coefficient, drainage basin area, maximum flow length, and maximum flow gradient. Elevation influences geomorphological and geological evolution, shaping the natural disaster background and indirectly regulating the spatial distribution of human activities. Slope is highly related to the formation and development of debris flows, with suitable slopes facilitating their initiation. Aspect affects microclimatic conditions, which in turn influence vegetation and rock weathering, controlling material availability. The topographic wetness index describes how terrain controls runoff generation and saturation source areas. The stream power index reflects the erosive force of flowing water. Topographic relief indicates the degree of surface erosion and tectonic activity. The drainage basin shape coefficient, drainage basin area, maximum flow length, and maximum flow gradient are the basic parameters for calculating flow paths and velocities, determining debris flow probability and scale.
- (2)
- Geology. This includes seismic nucleation density and fault distance. Earthquakes and tectonic faults weaken rock and soil strength, producing loose material that serves as debris flow sources.
- (3)
- Environmental factors. These include the normalized difference vegetation index, soil erosion, land use, and annual rainfall. The normalized difference vegetation index reflects vegetation cover, which promotes soil and water conservation and reduces debris flow sources. Soil erosion indicates surface susceptibility to erosion. Land use alters runoff and infiltration processes, potentially triggering debris flows. Annual rainfall is one of the primary triggers for debris flow events.
- (4)
- Human activities. These include distance to road and population statistics. Road construction along riverbanks can induce slope instability and accelerate loose material accumulation, raising debris flow risk. Population statistics reflect human activity intensity but are often highest in flatter areas, where geological conditions tend to inhibit debris flow development.
3.5. Overview of Machine Learning Models
3.5.1. Random Forest
3.5.2. Support Vector Machine
3.5.3. Extreme Gradient Boosting
3.5.4. Model Accuracy Verification
4. Results
4.1. Hyperparameter Optimization
4.2. Debris Flow Susceptibility Mapping in Songpan
4.3. Factor Importance
4.4. Debris Flow Susceptibility Mapping in Mao County
5. Discussion
5.1. Factor Importance Analysis
5.2. Best Model
5.3. Regional Adaptability Challenges in Transfer Learning
5.4. Limitations and Future Work
6. Conclusions
- (1)
- In the Songpan region, high landslide susceptibility areas are primarily located in the central, southern, and northeast–southeast transition zones of the study area, while low-susceptibility areas are concentrated in the northwest plateau region. In the Mao County region, high-susceptibility areas are concentrated in the central fault basin, southern deeply cut river valleys, and along the eastern fault zone in a strip-like distribution. Low-susceptibility areas are mainly found in the western plateau and northern folded mountains. This spatial distribution pattern highly matches the spatial distribution characteristics of landslide disaster points, thus validating the scientific and reliable evaluation method used in this study.
- (2)
- The study results highlight the significant influences of factors such as elevation, seismic nucleation density, population density, and distance to roads on landslide susceptibility, further revealing the main controlling factors of landslide disasters in the region.
- (3)
- Compared to the Support Vector Machine model and Extreme Gradient Boosting model, the Random Forest model demonstrated better applicability in both the Songpan and Mao County regions. It exhibited greater advantages in landslide susceptibility prediction tasks and is a more suitable choice for landslide susceptibility analysis in the complex geological environment of western Sichuan. This provides a cross-regional adaptive technical framework and quantitative evaluation paradigm for risk prevention and control in typical geological disaster-prone areas, such as active regions influenced by monsoon climates.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Type | Source of Information | Spatial Resolution |
---|---|---|
Debris Flow Hazard Spatial Dataset | The Resources and Environmental Sciences Data Center of the Chinese Academy of Sciences | Vector data |
Digital Elevation Model (DEM) | 12.5 m | |
Road Network Vector Data | Vector data | |
Land Use Raster Data | 30 m | |
Geological Fault Line Vectors | Vector data | |
Seismic Hazard Point Distributions | Vector data | |
Annual Precipitation Raster Data | 1 km | |
Soil Erosion Type and Intensity Classification Rasters | 1 km | |
Normalized Difference Vegetation Index (NDVI) Raster Data | 250 m | |
Population Spatial Distribution | 1 km |
Conditioning Factors | Very Low | Low | Moderate | High | Very High |
---|---|---|---|---|---|
Elevation (m) | 1009.10–2416.90 | 2416.90–3120.80 | 3120.80–3578.33 | 3578.33–3983.07 | 3983.08–5496.46 |
Slope (°) | 0–14 | 14–24 | 24–32 | 32–43 | 43–86 |
Topographic Wetness Index | 0–4 | 4–6 | 6–9 | 9–14 | 14–33 |
Stream Power Index | 0–4.0 × 103 | 4.0 × 103–1.0 × 104 | 1.0 × 104–5.0 × 104 | 5.0 × 104–1.0 × 106 | 1.0 × 106–8.66 × 107 |
Drainage Basin Shape Coefficient | 0–0.25 | 0.25–0.5 | 0.5–0.75 | 0.75–1 | 1–478 |
Drainage Basin Area (km2) | 0–2.47 | 2.47–5.61 | 5.61–9.59 | 9.59–16.09 | 16.09–45.96 |
Maximum Flow Length (km) | 0–2.50 | 2.50–4.26 | 4.26–6.05 | 6.05–8.85 | 8.85–17.53 |
Maximum Flow Gradient | 0–0.127 | 0.127–0.204 | 0.204–0.293 | 0.29–0.401 | 0.401–0.732 |
Seismic Nucleation Density | 4.4 × 10−5–4.8 × 10−5 | 4.9 × 10−5–5.1 × 10−5 | 5.2 × 10−5–5.4 × 10−5 | 5.4 × 10−5–5.6 × 10−5 | 5.6 × 10−5–5.7 × 10−5 |
Fault Distance (km) | 0–4 | 4–10 | 10–16 | 16–22 | 22–30 |
Normalized Difference Vegetation Index | 0.082–0.410 | 0.411–0.596 | 0.597–0.729 | 0.730–0.808 | 0.809–0.912 |
Soil Erosion (kg/m2/a) | 0.015–12.94 | 12.94–42.02 | 42.02–87.26 | 87.26–166.42 | 166.42–411.99 |
Annual Rainfall (mm) | 685.31–734.83 | 734.83–758.33 | 758.33–790.23 | 790.23–826.32 | 826.32–899.34 |
Population Statistics (person/km2) | 0–2 | 2–10 | 10–30 | 30–70 | 70–214 |
Distance to Road (km) | 0–3 | 3–8 | 8–13 | 13–22 | 22–39 |
Model | Hyperparameter | Definition | Optimum |
---|---|---|---|
SVM | C | Regularization parameter that controls the trade-off between achieving a low error and keeping the model simple. Higher values reduce bias but may increase variance. | 10 |
gamma | Defines how far the influence of a single training example reaches. A lower value means a larger influence region. | scale | |
kernel | Specifies the kernel type to be used in the algorithm, affecting decision boundaries. | RBF | |
RF | n_estimators | The number of trees in the forest. More trees generally improve performance but increase computation time. | 200 |
max_depth | The maximum depth of each decision tree. If none, nodes are expanded until all leaves are pure. | 10 | |
min_samples_split | The minimum number of samples required to split an internal node. Higher values prevent overfitting. | 2 | |
min_samples_leaf | The minimum number of samples required to be at a leaf node. Higher values make the model more robust. | 1 | |
XGBoost | n_estimators | The number of boosting rounds. More rounds can improve performance but may lead to overfitting. | 100 |
max_depth | Maximum depth of a tree. Larger values capture more patterns but increase complexity. | 6 | |
learning_rate | Controls the step size in updating weights. Lower values lead to slower but more stable convergence. | 0.2 | |
subsample | Fraction of training samples used in each boosting iteration to prevent overfitting. | 1.0 |
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Li, T.; Huang, Q.; Chen, Q. Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China. Appl. Sci. 2025, 15, 7462. https://doi.org/10.3390/app15137462
Li T, Huang Q, Chen Q. Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China. Applied Sciences. 2025; 15(13):7462. https://doi.org/10.3390/app15137462
Chicago/Turabian StyleLi, Tiezhu, Qidi Huang, and Qigang Chen. 2025. "Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China" Applied Sciences 15, no. 13: 7462. https://doi.org/10.3390/app15137462
APA StyleLi, T., Huang, Q., & Chen, Q. (2025). Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China. Applied Sciences, 15(13), 7462. https://doi.org/10.3390/app15137462