Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning
Abstract
:1. Introduction
2. Research Area and Data Collection
2.1. Research Area
2.2. Data
3. Methodology
3.1. GeoDetector
3.2. Machine Learning Methods
3.3. Recursive Feature Elimination
3.4. Confusion Matrix
4. Modeling
- (1)
- Aggregating the seismic landslide evaluation parameters in the study area and establishing a spatial database.
- (2)
- Designing three kinds of samples for the training set, validation set, and test set of the machine learning model.
- (3)
- Analyzing the importance ranking of evaluation factors obtained from GeoDetector and nine machine learning methods.
- (4)
- Using recursive feature elimination means screening the evaluation factors; exploring the relationship between evaluation factors and seismic factors;
- (5)
- Exploring the relationship between evaluation factors and landslides.
4.1. Spatial Database
4.2. Sample Design
4.3. Ranking of Factor Importance
4.3.1. Evaluation Factors of GeoDetector
4.3.2. Evaluation Factors for Machine Learning Modeling
- (1)
- Logistic regression: iteration number, regularization type, convergence metric, and regularization factor are set to 100, L1, 0.0001, and 0.01084366, respectively.
- (2)
- XGBoost with binary optimization objective function: learning rate, maximum tree depth, minimum bifurcation weight, and L2 regularization coefficient are assigned to 0.3, 8, 6, and 0.5, respectively;
- (3)
- LightGBM with gbdt type algorithm: learning rate, maximum tree depth, and the maximum number of leaves, and maximum number of leaves are set to 0.001, 20, 50, 5, respectively;
- (4)
- RandomForest with metric Gini: minimum divergence purity gain, number of trees are assigned to 0.0, and 100, respectively;
- (5)
- AdaBoost: n_learning rate and n_estimators number of single models are set to 0.3, and 50, respectively;
- (6)
- GNB: var_smoothing 1 × 10−7;
- (7)
- CNB: alpha (additive (Laplace/Lidstone) smoothing): 0;
- (8)
- MLP with nonlinear logistic function: hidden layer width and iteration number are set to (30,30), and 20, respectively;
- (9)
- SVM: C (coefficient of regularization): 1.0, kernel (kernel type): rbf, tol (convergence metric): 0.001.
4.4. Factor Screening
4.5. Factor Analysis
4.5.1. Single Factor Analysis
4.5.2. Analysis of Factor Interactions
- (1)
- Non-linear weakening relationship: q(x1∩x2) < Min(q(x1), q(x2));
- (2)
- single-factor nonlinear weakening: Min(q(x1), q(x2)) < q(x1∩x2) < Max(q(x1), q(x2));
- (3)
- two-factor enhancement: q(x1∩x2) > Max(q(x1), q(x2)).
- (4)
- independent q(x1∩x2) = q(x1) + q(x2);
- (5)
- nonlinear enhancement: q(x1∩x2) > q(x1) +q(x2)).
5. Results
5.1. Model Validation
5.2. Optimized Landslide Susceptibility Mapping
6. Discussion
6.1. The Optimization of the Evaluation Factors of the Seismic Landslide
6.2. Impact of Effective Evaluation Factors on Seismic Landslides
6.3. Data Processing and Sampling in Landslide Prediction
7. Conclusions
- (i)
- The combination of machine learning models with the GeoDetector addressed the lack of spatial features in machine learning models and improved the interpretability of model outcomes. This offers a reference solution for subsequent machine learning model studies on regional issues.
- (ii)
- Through recursive screening of integrated evaluation factors across nine machine learning models, the effective factors identified were proven to be applicable across these models. This comprehensive evaluation approach overcame the limitation where effective factors initially screened in one way were only usable in that particular model, laying the foundation for establishing a regional master database of effective evaluation factors and offering new directions for formulating earthquake disaster prevention and mitigation plans.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Statistical Criteria | Meaning | Formula |
---|---|---|
Accuracy | the proportion of true results (both true positives and true negatives) in the total cases examined. | |
Sensitivity | the proportion of actual positives that were correctly identified. | |
Specificity | the proportion of actual positives that were correctly identified. | |
Positive predictive value | the proportion of positive identifications that were correct. | |
Negative predictive value | the proportion of negative identifications that were correct. | |
-value | the harmonic mean of precision and sensitivity. |
Parameters | Meaning | Acquisition Means |
---|---|---|
Elevation | Surface to sea level height. | ASTER satellite 30 m data for projection analysis. |
Slope | The degree of steepness of the surface unit. | ArcGIS:3D Analyst tools-Raster Surface-Slope. |
Aspect | The direction of the projection of the normal to the slope on the horizontal plane. | ArcGIS:3D Analyst tools-Raster Surface. -Aspect. |
RDLS | The height difference between the highest and lowest point in the defined area. | ArcGIS: Spatial Analyst Tools-Neighborhood -point Statistics. |
Slope length | The maximum horizontal projection length of the trajectory between the fixed point upstream and the beginning of the flow. | |
TRI | The ratio between their projected areas in the specified area. | |
TWI | Physical indicators of the influence of regional topography on runoff direction and accumulation. | Note: SCA is the sink flow per unit area. |
SPI | Quantitative description of the erosive capacity of surface water. | |
STI | Quantitative description of surface water sand transport capacity. | |
Curvature | The slope of the surface slope | ArcGIS:3D Analyst tools-Raster Surface-Curvature |
Profile Curvature | Second-order derivative along the steepest descending slope | ArcGIS:3D Analyst tools-Raster Surface-Curvature |
Plan Curvature | Second-order derivative perpendicular to the downward gradient | ArcGIS:3D Analyst tools-Raster Surface-Curvature |
Evaluation Factors | Class | Classification Standards |
---|---|---|
Elevation (m) | 8 | 1. <2350; 2. 2350–2469; 3. 2469–2572; 4. 2572–2672; 5. 2672–2883; 6. 2883–3028; 7. 3028–3339; 8. >3339. |
Lithology | 5 | 1. Devonian (D); 2. Permian (P); 3. Paleocene (E); 4. Neocene (N); 5. Quaternary (Q). |
Distance from roads | 7 | 1. <500; 2. 500–1000; 3. 1000–1500; 4. 1500–2000; 5. 2000–2500; 6. 2500–3000; 7. >3000. |
Aspect | 9 | 1. Flat; 2. North; 3. Northwest; 4. East; 5. Southwest; 6. South; 7. Southwest; 8. West; 9. Northwest |
RDLS | 8 | 1. 0–42; 2. 42–72; 3. 72–95; 4. 95–117; 5. 117–140; 6. 140–167; 7. 167–202; 8. 202–314. |
Slope length | 6 | 1. <4683; 2. 4683–5003; 3. 5003–6941; 4. 6941–18,648; 5. 18,648–89,393; 6. >89,393. |
TRI | 6 | 1. <1.05; 2. 1.05–1.1; 3. 1.1–1.17; 4. 1.17–1.27; 5. 1.27–1.4; 6. >1.4. |
TWI | 7 | 1. <2.7; 2. 2.7–5.1; 3. 5.1–6.9; 4. 6.9–9.4; 5. 9.4–11.4; 6. 11.4–13.3; 7. >13.3. |
SPI | 6 | 1. <6520; 2. 6520–13,041; 3. 13,041–19,562; 4. 19,562–39,124; 5. 39,124–136,935; 6. >136,935. |
STI | 7 | 1. <100; 2. 100–200; 3. 200–300; 4. 300–400; 5. 400–500; 6. 500–600; 7. >600. |
PGA | 7 | 1. <0.3; 2. 0.3–0.35; 3. 0.35–0.4; 4. 0.4–0.45; 5. 0.45–0.5; 6. 0.5–0.55; 7. >0.55. |
Curvature | 8 | 1. <−3; 2. −3 to −2; 3. −2 to −1; 4. −1 to 0; 5. 0–1; 6. 1–2; 7. 2–3; 8. >3. |
Profile Curvature | 8 | 1. <−2; 2. −2 to −1; 3. −1 to −0.5; 4. −0.5 to 0; 5. 0–0.5; 6. 0.5–1; 7. 1–2; 8. >2. |
Plan Curvature | 8 | 1. <−2; 2. −2 to −1; 3. −1 to −0.5; 4. −0.5 to 0; 5. 0–0.5; 6. 0.5–1; 7. 1–2; 8. >2. |
Distance from faults | 8 | 1. <500; 2. 500–1000; 3. 1000–1500; 4. 1500–2000; 5. 2000–2500; 6. 2500–3000; 7. 3000–3500; 8. >3000. |
NDVI | 8 | 1. <0.25; 2. 0.25–0.3; 3. 0.3–0.35; 4. 0.35–0.4; 5. 0.4–0.45; 6. 0.45–0.5; 7. 0.5–0.55; 8. >0.55. |
Distance from rivers | 7 | 1. <1000; 2. 1000–2000; 3. 2000–3000; 4. 3000–4000; 5. 4000–5000; 6. 5000–6000; 7. >6000. |
Land cover | 5 | 1. Arable land; 2. Forest; 3. Grassland; 4. Land for waters and water conservancy facilities; 5. Artificial surface. |
Slope (°) | 9 | 1. <5; 2. 5–10; 3. 10–15; 4. 15–20; 5. 20–25; 6. 25–30; 7. 30–35; 8. 35–40; 9. >40. |
POI kernel density | 7 | 1. <0.2; 2. 0.2–0.4; 3. 0.4–0.6; 4. 0.6–0.8; 5. 0.8–1; 6. 1–2; 7. >2. |
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He, H.; Wang, W.; Wang, Z.; Li, S.; Chen, J. Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning. Sustainability 2024, 16, 3828. https://doi.org/10.3390/su16093828
He H, Wang W, Wang Z, Li S, Chen J. Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning. Sustainability. 2024; 16(9):3828. https://doi.org/10.3390/su16093828
Chicago/Turabian StyleHe, Hailang, Weiwei Wang, Zhengxing Wang, Shu Li, and Jianguo Chen. 2024. "Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning" Sustainability 16, no. 9: 3828. https://doi.org/10.3390/su16093828