Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area Profile
2.2. Frequency Ratio Analysis of Environmental Factors
2.3. The Principle of the Machine Learning Methods
- (1)
- RF can be understood as the organic integration of bagging (bootstrap aggregating) ensemble learning and the decision tree algorithm. The basic idea is to construct multiple decision trees by randomly selecting training samples, and the output category is determined by the mode or average of the predicted values of these single decision trees. That is, the prediction results of unknown samples are determined by the principle of majority voting, and the information of multiple decision trees is integrated to improve the accuracy of classification and the stability of the model. Its expression is as follows:
- (2)
- SVM is a supervised learning method. The basic idea is to map the samples in the input space to a high-dimensional feature space through nonlinear transformation. Then, the optimal classification surface in the feature space that linearly separates the samples is obtained. Based on a set of linearly separable vectors xi (i = 1, 2..., n), including 10 environmental factors and their corresponding output class yi, the landslide classification is distinguished by the maximum clearance of the n-dimensional hyperplane. Its expressions are as follows:
- (3)
- Log is a kind of generalized linear regression analysis model that fits a logical function to forecast the probability of an event occurring. Its expression is as follows:
2.4. The Principle of the SINMAP Model
2.5. Dataset
- (1)
- The machine learning method dataset
- (2)
- The dataset of the SINMAP model
2.6. Model Performance Evaluation and Validation
3. Results
3.1. Frequency Ratio Analysis of Environmental Factors
3.2. Multicollinearity Analysis
3.3. Analysis for the Model Results
3.3.1. Model Performance and Validation
3.3.2. Mapping and Comparison of the Sensitivity of Shallow Landslides
4. Discussion
4.1. Characteristics of Environmental Factors for Shallow Landslides
4.2. Difference in Sensitivity Mapping of Shallow Landslides Predicted by the Models
4.3. Limitations and Implications of This Study
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Class | Stability Index Using Factor Safety | Characteristics |
---|---|---|
1 | <0.5 | Very High Susceptibility |
2 | 0.5–1.0 | High Susceptibility |
3 | 1.0–1.25 | Moderate Susceptibility |
4 | 1.25–1.5 | Low Susceptibility |
5 | >1.5 | Very Low Susceptibility |
Environmental Factor Type | Factor | Data Sources | Data Resolution | Classification Method |
---|---|---|---|---|
Topographic and Geomorphic Factors | DEM | ALOS Satellite | 12.5 m | Natural Break |
Slope Gradient | ||||
Slope Aspect | ||||
Topographic Relief | ||||
Plane Curvature | ||||
Profile Curvature | ||||
Hydrological Environmental Factors | TWI | |||
SPI | ||||
STI | ||||
Rainfall Erosivity | Geospatial Data Cloud | 30.0 m | ||
Land Cover Factors | NDVI | |||
Land Use Type | National Earth System Science Data Center | Equal Interval |
ρs (kg·m−3) | T/R (m) | C (N·m−2) | φ (°) | |||
---|---|---|---|---|---|---|
entry 1 1350 | min | max | min | max | min | max |
100 | 3000 | 0.35 | 0.54 | 15 | 25 |
Factors | Collinearity Statistics | |
---|---|---|
VIF | TOL | |
DEM | 2.36 | 0.42 |
Slope Gradient | 1.81 | 0.55 |
Slope Aspect | 1.08 | 0.93 |
Plane Curvature | 1.36 | 0.73 |
Profile Curvature | 1.36 | 0.73 |
TWI | 1.58 | 0.64 |
STI | 1.42 | 0.70 |
Rainfall Erosivity | 2.84 | 0.35 |
NDVI | 1.50 | 0.67 |
Land Use Type | 1.05 | 0.95 |
Model | Accuracy Parameters | ||
---|---|---|---|
Sensitivity | Specificity | Accuracy | |
RF | 92.03% | 85.99% | 84.85% |
SVM | 87.87% | 76.60% | 82.23% |
Log | 83.71% | 66.75% | 79.38% |
SINMAP | 81.21% | 59.98% | 70.59% |
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Feng, L.; Guo, M.; Wang, W.; Chen, Y.; Shi, Q.; Guo, W.; Lou, Y.; Kang, H.; Chen, Z.; Zhu, Y. Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling. Sustainability 2023, 15, 6. https://doi.org/10.3390/su15010006
Feng L, Guo M, Wang W, Chen Y, Shi Q, Guo W, Lou Y, Kang H, Chen Z, Zhu Y. Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling. Sustainability. 2023; 15(1):6. https://doi.org/10.3390/su15010006
Chicago/Turabian StyleFeng, Lanqian, Mingming Guo, Wenlong Wang, Yulan Chen, Qianhua Shi, Wenzhao Guo, Yibao Lou, Hongliang Kang, Zhouxin Chen, and Yanan Zhu. 2023. "Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling" Sustainability 15, no. 1: 6. https://doi.org/10.3390/su15010006