Landslide Susceptibility Evaluation Based on a Coupled Informative–Logistic Regression Model—Shuangbai County as an Example
Abstract
:1. Introduction
2. Overview of the Study Area
3. Research Methods and Data Sources
3.1. Research Methodology
3.1.1. Information Volume Model
3.1.2. Logistic Regression Model
3.1.3. Informativeness–Logistic Regression Model
3.2. Data Sources
4. Evaluation Unit and Impact Factor Analysis
4.1. Evaluation Module
4.2. Analysis of Impact Factors
- (1)
- Topography
- (2)
- Hydrological environment
- (3)
- Geological structure and ground cover
- (4)
- Stratigraphic lithology
- (5)
- Human engineering activities
4.3. Correlation Analysis of Impact Factors
5. Analysis of the Landslide Susceptibility Model Evaluation
5.1. Information Volume Model Evaluation
5.2. Informative–Logistic Regression Model Evaluation
5.3. Comparative Analysis of Evaluation Model Accuracy Assessments
6. Discussion
7. Conclusions
- (1)
- An index system comprising slope direction, NDVI, distance from water systems, plane curvature, profile curvature, stratigraphic lithology, distance from roads, slope, distance from faults, elevation, and average annual rainfall was established to construct the landslide susceptibility evaluation models. The IM model and the IM–LR coupled model were utilized to assess susceptibility and derive susceptibility zones. The density of landslide points within each zone was calculated, revealing that the point density increases in the less-susceptible, low-susceptible, medium-susceptible, and high-susceptible areas for both models, aligning with the reasonableness of the models. Notably, the IM–LR coupled model exhibits higher point density in the medium- and high-susceptibility zones compared to the IM model, indicating that the susceptibility zoning of the IM–LR coupled model better approximates reality.
- (2)
- Analysis of the susceptibility zoning map indicates that the high-susceptibility zone is predominantly distributed in the central areas of Ejia Township, the eastern part of Anlongbao Township, Fabiao Township, the southern part of Dazhuang Township, and regions near rivers and roads in Toudian Township. These areas feature lower elevation, less vegetation cover, and increased human engineering activities, contributing to unstable slopes. In contrast, the less-susceptibility and low-susceptibility zones are primarily located in the western part of Ejia Township, the southern part of Anlongbao Township, Duda Township, and the southern part of Damaidi Township. These areas have higher elevation, limited human engineering activities, and more stable slopes.
- (3)
- The AUC values of the two models were obtained from the ROC curves. The AUC value of the IM–LR coupled model, 0.800, was larger than that of the IM model, 0.74. Additionally, the accuracy and precision of the IM model were lower than those of the IM–LR model, as obtained from the confusion matrix. These results indicate that the IM–LR coupling model performs better in evaluating the susceptibility of the study area and can provide a more accurate landslide susceptibility zoning for Shuangbai County. This model can serve as a scientific basis for the planning of related departments and a reference for researchers evaluating susceptibility in similar areas.
- (4)
- Five factors, namely distance to fault, distance from water system, NDVI, slope, and elevation, have significant contributions to the landslide hazard vulnerability in Shuangbai County. Landslides in this region are mainly distributed near roads, water systems, and faults. The probability of landslides decreases with increasing distance from these areas due to relatively broken rocks and poor slope stability. Most landslides occur in the areas of soft rock, loose rock, and softer rock. Additionally, landslides are more likely to occur on shady slopes with medium vegetation cover and significant changes in surface undulation within the elevation range of 1659 m to 1914 m, where the slope is approximately 20°. These findings serve as a valuable reference for geotechnical workers and practitioners in understanding the development characteristics of landslides in the study area.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Name | Data Source |
---|---|
Elevation/(m) slope/(°) slope direction/(°) | Geospatial Data Cloud Download DEM (30 m × 30 m) |
Geospatial Data Cloud Download DEM (30 m × 30 m) | |
Geospatial Data Cloud Download DEM (30 m × 30 m) | |
curvature | Geospatial Data Cloud Download DEM (30 m × 30 m) |
Road network | National Geographic Information Resource Catalog System Download Vector Data (1:200,000) |
River Distribution | National Geographic Information Resource Catalog System Download Vector Data (1:200,000) |
NDVI | Geospatial Data Cloud Download Landsat8 Remote Sensing Imagery (30 m) |
Stratigraphic lithology | 1:1 million geological map |
Tectonic distribution | 1:200,000 geological map |
Rainfall/(mm) | National Qinghai–Tibet Plateau Science Data Center (1 km) |
Evaluation Factors | Grading | Number of Graded Grids/(pcs) | Number of Landslides/(Places) | LRPD | I |
---|---|---|---|---|---|
Elevation/m | 537–1082 | 421,577 | 7 | 0.485030 | −0.723544 |
1082–1391 | 730,377 | 26 | 1.039858 | 0.039084 | |
1391–1659 | 1,017,808 | 38 | 1.090600 | 0.086728 | |
1659–1914 | 1,106,020 | 50 | 1.320550 | 0.278048 | |
1914–2284 | 753,349 | 26 | 1.008150 | 0.008117 | |
2284–3017 | 294,111 | 1 | 0.099320 | −2.309409 | |
Slope/(°) | <10 | 445,873 | 15 | 0.982717 | −0.017434 |
10–20 | 1,282,431 | 63 | 1.435009 | 0.361171 | |
20–30 | 1,552,550 | 47 | 0.884301 | −0.122958 | |
30–40 | 839,113 | 22 | 0.765862 | −0.266754 | |
40–50 | 184,117 | 1 | 0.158655 | −1.841020 | |
>50 | 19,161 | 0 | 0.000000 | 0.000000 | |
Slope direction/(°) | −1.0 | 1825 | 0 | 0.000000 | 0.000000 |
0–22.5 | 504,695 | 16 | 0.926060 | −0.076816 | |
22.5–67.5 | 537,602 | 20 | 1.086719 | 0.083163 | |
67.5–112.5 | 539,063 | 22 | 1.192151 | 0.175759 | |
112.5–157.5 | 577,093 | 17 | 0.860501 | −0.150241 | |
157.5–202.5 | 531,144 | 15 | 0.824949 | −0.192434 | |
202.5–247.5 | 544,319 | 17 | 0.912312 | −0.091773 | |
247.5–292.5 | 532,600 | 15 | 0.822694 | −0.195171 | |
292.5–337.5 | 554,904 | 26 | 1.368685 | 0.313851 | |
Distance from road/(km) | <0.5 | 2,529,368 | 104 | 1.201072 | 0.183215 |
0.5–1.0 | 1,176,748 | 31 | 0.769531 | −0.261974 | |
1.0–1.5 | 437,602 | 10 | 0.667527 | −0.404176 | |
1.5–2.0 | 121,097 | 2 | 0.482441 | −0.728896 | |
2.0–2.5 | 33,075 | 1 | 0.883177 | −0.124229 | |
2.5–3.0 | 12,796 | 0 | 0.000000 | 0.000000 | |
>3.0 | 12,556 | 0 | 0.000000 | 0.000000 | |
Distance from water system/(m) | <300 | 1,803,140 | 80 | 1.296010 | 0.259291 |
300–600 | 1,344,636 | 42 | 0.912415 | −0.091660 | |
600–900 | 773,050 | 21 | 0.793523 | −0.231272 | |
900–1200 | 308,229 | 4 | 0.379083 | −0.969999 | |
1200–1500 | 79,096 | 1 | 0.369313 | −0.996110 | |
>1500 | 15,093 | 0 | 0.000000 | 0.000000 | |
Average annual rainfall/(mm) | 628–686 | 977,111 | 39 | 1.165919 | 0.153510 |
686–721 | 1,271,026 | 61 | 1.401920 | 0.337843 | |
721–756 | 1,037,774 | 24 | 0.675548 | −0.392231 | |
756–801 | 861,463 | 17 | 0.576448 | −0.550870 | |
801–899 | 175,867 | 7 | 1.162682 | 0.150730 | |
NDVI | 0.33–0.48 | 199,041 | 2 | 0.293519 | −1.225814 |
0.49–0.58 | 572,765 | 20 | 1.020003 | 0.019806 | |
0.58–0.65 | 1,021,344 | 59 | 1.687438 | 0.523212 | |
0.65–0.71 | 1,420,985 | 49 | 1.007290 | 0.007263 | |
0.71–0.83 | 1,109,108 | 18 | 0.474074 | −0.746391 | |
Distance from fault/(km) | 0–1.0 | 1,090,368 | 47 | 1.259136 | 0.230426 |
1.0–2.0 | 928,410 | 31 | 0.975371 | −0.024937 | |
2.0–3.0 | 737,673 | 22 | 0.871178 | −0.137909 | |
3.0–4.0 | 535,742 | 23 | 1.254066 | 0.226391 | |
4.0–5.0 | 314,366 | 10 | 0.929208 | −0.073423 | |
>5.0 | 716,685 | 15 | 0.611380 | −0.492037 | |
Stratigraphic lithology | weak sandwich | 1,232,644 | 113 | 1.001689 | 0.001688 |
softer rock | 360,826 | 8 | 1.773527 | 0.572970 | |
harder rock | 1,729,174 | 8 | 0.951505 | −0.049710 | |
soft rock | 69,896 | 7 | 1.752480 | 0.561032 | |
hard rock | 73,339 | 9 | 0.588649 | −0.529926 | |
loose rock | 116,065 | 3 | 1.003875 | 0.003868 | |
Plane curvature | 0–14.5 | 834,217 | 27 | 0.945437 | −0.056109 |
14.5–25.8 | 1,014,325 | 37 | 1.065546 | 0.063488 | |
25.8–38.0 | 868,071 | 33 | 1.110469 | 0.104782 | |
38.0–51.9 | 597,551 | 26 | 1.271001 | 0.239805 | |
51.9–67.7 | 461,393 | 8 | 0.506485 | −0.680261 | |
67.7–82.2 | 547,683 | 17 | 0.906708 | −0.097935 | |
Profile curvature | 0–4.6 | 911,400 | 41 | 1.314082 | 0.273138 |
4.6–8.2 | 1,233,856 | 44 | 1.041684 | 0.040838 | |
8.2–11.8 | 994,346 | 30 | 0.881316 | −0.126340 | |
11.8–15.9 | 676,942 | 21 | 0.906182 | −0.098515 | |
15.9–21.4 | 377,141 | 9 | 0.697086 | −0.360847 | |
21.4–45.7 | 129,555 | 3 | 0.676416 | −0.390947 |
A | B | C | D | E | F | G | H | I | J | K | |
---|---|---|---|---|---|---|---|---|---|---|---|
A | 1 | ||||||||||
B | 0.051 | 1 | |||||||||
C | −0.009 | 0.051 | 1 | ||||||||
D | 0.009 | −0.036 | 0.163 | 1 | |||||||
E | −0.059 | 0.125 | −0.001 | −0.181 | 1 | ||||||
F | −0.008 | 0.167 | −0.016 | −0.042 | −0.026 | 1 | |||||
G | 0.023 | 0.071 | −0.032 | −0.042 | 0.283 | −0.034 | 1 | ||||
H | 0.007 | 0.092 | −0.013 | −0.021 | 0.167 | 0.152 | −0.001 | 1 | |||
I | 0.217 | 0.299 | 0.025 | 0.212 | −0.298 | 0.041 | 0.029 | 0.115 | 1 | ||
J | −0.052 | −0.048 | −0.112 | −0.073 | −0.033 | −0.001 | −0.002 | −0.099 | −0.071 | 1 | |
K | −0.076 | 0.158 | 0.114 | −0.299 | 0.259 | 0.142 | −0.022 | 0.072 | −0.221 | −0.073 | 1 |
Factor | Regression Coefficient | Significance |
---|---|---|
Slope direction | 0.791 | 0 |
NDVI | 1.170 | 0.017 |
Distance from water system | 1.183 | 0.007 |
Plane curvature | 0.730 | 0.028 |
Profile curvature | 0.465 | 0 |
Stratigraphic lithology | 0.217 | 0.011 |
Distance from road | 0.836 | 0 |
Slope | 0.999 | 0.003 |
Distance to fault | 1.254 | 0.003 |
Elevation | 0.909 | 0.002 |
Average annual rainfall | −0.122 | 0 |
Constants | 0.053 | 0 |
Projected Value | |||
---|---|---|---|
0 | 1 | ||
real value | 0 | 98 | 50 |
1 | 44 | 104 |
Projected Value | |||
---|---|---|---|
0 | 1 | ||
real value | 0 | 105 | 43 |
1 | 39 | 109 |
Evaluation Model | Vulnerability Zones | No. of Landslides (Places) | Landslide Site Density (Places/ ) |
---|---|---|---|
IM | Non-prone areas | 2 | 0.006 |
Low-susceptibility area | 9 | 0.010 | |
Medium-prone areas | 54 | 0.037 | |
Highly susceptible areas | 83 | 0.068 | |
IM–LR | Non-prone areas | 2 | 0.005 |
Low-susceptibility area | 10 | 0.012 | |
Medium-prone areas | 43 | 0.030 | |
Highly susceptible areas | 93 | 0.075 |
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Wang, H.; Xu, J.; Tan, S.; Zhou, J. Landslide Susceptibility Evaluation Based on a Coupled Informative–Logistic Regression Model—Shuangbai County as an Example. Sustainability 2023, 15, 12449. https://doi.org/10.3390/su151612449
Wang H, Xu J, Tan S, Zhou J. Landslide Susceptibility Evaluation Based on a Coupled Informative–Logistic Regression Model—Shuangbai County as an Example. Sustainability. 2023; 15(16):12449. https://doi.org/10.3390/su151612449
Chicago/Turabian StyleWang, Haishan, Jian Xu, Shucheng Tan, and Jinxuan Zhou. 2023. "Landslide Susceptibility Evaluation Based on a Coupled Informative–Logistic Regression Model—Shuangbai County as an Example" Sustainability 15, no. 16: 12449. https://doi.org/10.3390/su151612449