An Ensemble Broad Learning System (BLS) for Evaluating Landslide Susceptibility in Taiyuan City, Northern China
Abstract
:1. Introduction
2. Study Area and Data Used
2.1. Study Area
2.2. Data Used
3. Modeling Approaches
3.1. Training and Validation Datasets
3.2. Broad Learning System (BLS)
3.3. Support Vector Machine (SVM)
3.4. Classification and Regression Trees (CART)
3.5. Adaptive Boosting (AdaBoost)
3.6. Selection of Landslide Conditioning Factors
3.7. Evaluation of the Models’ Performance
3.7.1. Statistical Index-Based Evaluations
3.7.2. Receiver Operating Characteristic (ROC)
4. Results and Discussion
4.1. Relationships between Landslides and the Related Factors
4.2. Selection of Landslide Conditioning Factors
4.3. Building the Ensemble Models and Constructing Landslide Susceptibility Maps
4.4. Validation of the Landslide Susceptibility Maps
5. Discussion
6. Conclusions
- (1)
- According to the results of the FR, most landslides occurred at elevations of 941–1193 m, slopes of 6, distances to rivers of <500 m, distances to roads of <800 m, slopes with a southeast aspect, SPI values of 2.4–4.5, TWI values of <5, NDVI values of 0.15–0.23, plan curvatures of −0.7–−0.3, surface cutting depths of 6–9.5, terrain roughness values of 1.03–1.11 and terrain relief values of 11.5–18.5.
- (2)
- In total, 12 landslide impact factors were identified and assessed on the basis of their VI values. The most important impact factor was elevation, followed by NDVI, TWI, curvature, distance to rivers, distance to roads, SPI, slope aspect, terrain roughness index, slope, surface cutting depth and terrain relief.
- (3)
- The three models (CART-AdaBoost, SVM-AdaBoost, and BLS-AdaBoost) were evaluated and compared by statistical methods and AUC. All three methods had good results, but it is evident from the results that the method proposed in this study, utilizing ensemble BLS, outperformed the other two methods.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Factors | Class | Percentage of Domain % | Percentage of Landslides % | FR |
---|---|---|---|---|
Elevation | <941 | 21.15 | 4.39 | 0.21 |
941–1193 | 17.88 | 52.63 | 2.94 | |
1193–1416 | 28.53 | 33.33 | 1.17 | |
1416–1664 | 25.15 | 8.77 | 0.35 | |
>1664 | 7.29 | 0.88 | 0.12 | |
Slope | <3 | 25.27 | 17.54 | 0.69 |
3–6 | 20.97 | 23.68 | 1.13 | |
6–9 | 21.17 | 32.47 | 1.53 | |
9–13 | 19.84 | 20.18 | 1.02 | |
>13 | 12.76 | 6.14 | 0.48 | |
Distance to rivers | <500 | 12.68 | 24.56 | 1.94 |
500–1000 | 12.01 | 14.91 | 1.24 | |
1000–1500 | 11.29 | 8.77 | 0.78 | |
1500–2000 | 10.44 | 8.77 | 0.84 | |
>2000 | 53.58 | 42.98 | 0.80 | |
Distance to roads | <800 | 28.81 | 39.47 | 1.37 |
800–1600 | 21.20 | 21.93 | 1.03 | |
1600–2400 | 15.59 | 16.67 | 1.07 | |
2400–3200 | 11.44 | 10.53 | 0.92 | |
>3200 | 22.97 | 11.40 | 0.50 | |
Slope aspect | Flat | 6.01 | 4.39 | 0.73 |
North | 11.21 | 6.14 | 0.55 | |
Northeast | 12.17 | 11.40 | 0.94 | |
East | 11.93 | 13.16 | 1.10 | |
Southeast | 12.51 | 20.18 | 1.61 | |
South | 12.19 | 12.28 | 1.01 | |
Southwest | 12.01 | 14.04 | 1.17 | |
West | 10.76 | 6.14 | 0.57 | |
Northwest | 11.20 | 12.28 | 1.10 | |
SPI | <−3 | 31.14 | 4.39 | 0.14 |
−3–1.5 | 33.80 | 6.14 | 0.18 | |
1.5–2.5 | 27.36 | 34.21 | 1.25 | |
2.5–4.5 | 7.70 | 47.37 | 6.15 | |
>4.5 | 6.64 | 7.89 | 1.19 | |
TWI | <5 | 20.52 | 21.93 | 1.07 |
5–7 | 41.09 | 42.11 | 1.02 | |
7–12 | 27.87 | 27.19 | 0.98 | |
12–17 | 6.39 | 5.26 | 0.82 | |
>17 | 4.13 | 3.51 | 0.85 | |
NDVI | <0.15 | 25.70 | 28.95 | 1.13 |
0.15–0.23 | 30.51 | 42.11 | 1.38 | |
0.23–0.28 | 27.59 | 21.05 | 0.76 | |
0.28–0.36 | 9.96 | 7.02 | 0.70 | |
>0.36 | 6.23 | 0.88 | 0.14 | |
Plan curvature | <−0.7 | 5.33 | 1.75 | 0.33 |
−0.7–−0.3 | 15.33 | 27.19 | 1.77 | |
−0.3–0.02 | 43.05 | 43.86 | 1.01 | |
0.02–0.6 | 36.29 | 24.56 | 0.68 | |
>0.6 | 5.75 | 2.63 | 0.46 | |
Surface cutting depth | <3.5 | 29.12 | 21.05 | 0.72 |
3.5–6 | 17.13 | 23.68 | 1.38 | |
6–9.5 | 20.77 | 29.82 | 1.44 | |
9.5–12.5 | 15.58 | 12.28 | 0.79 | |
>12.5 | 17.40 | 13.16 | 0.76 | |
Terrain roughness | <1.01 | 38.83 | 32.46 | 0.84 |
1.01–1.03 | 16.07 | 21.93 | 1.36 | |
1.03–1.11 | 17.50 | 26.32 | 1.50 | |
1.11–1.16 | 15.21 | 13.16 | 0.86 | |
>1.16 | 12.39 | 6.14 | 0.50 | |
Terrain relief | <7 | 26.96 | 18.42 | 0.68 |
7–11.5 | 16.42 | 16.67 | 1.01 | |
11.5–18.5 | 22.55 | 37.72 | 1.67 | |
18.5–28 | 20.18 | 21.05 | 1.04 | |
>28 | 13.90 | 6.14 | 0.45 |
Landslide Conditioning Factors | VI |
---|---|
Elevation | 0.219 |
NDVI | 0.198 |
TWI | 0.103 |
Curvature | 0.099 |
Distance to rivers | 0.087 |
Distance to roads | 0.073 |
SPI | 0.059 |
Slope aspect | 0.046 |
Terrain roughness index | 0.042 |
slope | 0.023 |
Surface cutting depth | 0.023 |
Terrain relief | 0.021 |
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Zhao, D.; Ren, P.; Feng, G.; Ren, H.; Li, Z.; Wang, P.; Han, B.; Dong, S. An Ensemble Broad Learning System (BLS) for Evaluating Landslide Susceptibility in Taiyuan City, Northern China. Appl. Sci. 2023, 13, 8409. https://doi.org/10.3390/app13148409
Zhao D, Ren P, Feng G, Ren H, Li Z, Wang P, Han B, Dong S. An Ensemble Broad Learning System (BLS) for Evaluating Landslide Susceptibility in Taiyuan City, Northern China. Applied Sciences. 2023; 13(14):8409. https://doi.org/10.3390/app13148409
Chicago/Turabian StyleZhao, Dekang, Peiyuan Ren, Guorui Feng, Henghui Ren, Zhenghao Li, Pengwei Wang, Bing Han, and Shuning Dong. 2023. "An Ensemble Broad Learning System (BLS) for Evaluating Landslide Susceptibility in Taiyuan City, Northern China" Applied Sciences 13, no. 14: 8409. https://doi.org/10.3390/app13148409
APA StyleZhao, D., Ren, P., Feng, G., Ren, H., Li, Z., Wang, P., Han, B., & Dong, S. (2023). An Ensemble Broad Learning System (BLS) for Evaluating Landslide Susceptibility in Taiyuan City, Northern China. Applied Sciences, 13(14), 8409. https://doi.org/10.3390/app13148409