Slope Rock and Soil Mass Movement Geological Hazards Susceptibility Evaluation Using Information Quantity, Deterministic Coefficient, and Logistic Regression Models and Their Comparison at Xuanwei, China
Abstract
:1. Introduction
2. Overview of the Study Area
3. Research Methods
3.1. Certainty Factors Methods (CF)
3.2. Information Methods (I)
3.3. Logistic Regression Model (LR)
3.4. Certainty Factors–Logistic Regression Model (CF+LR)
3.5. Informativeness–Logistic Regression Model (I+LR)
4. Susceptibility Evaluation
4.1. Selection and Grading of the Evaluation Factor
- (1)
- Elevation: Elevation represents the macroscopic landform within a specific area. Numerous research findings indicate that geological hazard occurrence and elevation distribution exhibit significant regional patterns [15]. Simultaneously, elevation largely determines the movement potential energy of the hazard body. As the elevation increases, the dynamic potential energy accumulated post-sliding also increases, leading to a greater impact on the hazard-prone area and more significant losses as a result. Considering the spatial distribution characteristics of geological hazard points and elevation in the study area, elevation is divided into five grades: <1000 m, 1000 m–1200 m, 1200 m–1500 m, 1500 m–2000 m, and >2000 m (Figure 2a).
- (2)
- Gradient: In the study area, the slope magnitude is intimately connected to the extent of geological hazards. The displacement and impact force of the hazard body largely depends on the slope size. Moreover, the slope factor significantly controls the formation mechanism of geological hazards and the critical point of anti-slipping for the sliding body. The slope of the study area is divided into four grades: <10°, 10°–30°, 30°–50°, and >50° (Figure 2b).
- (3)
- Slope orientation: When vegetation coverage is equal, the sunny slope exhibits ample water and heat, leading to the saturation of internal water within the rock mass. This saturation, coupled with water infiltration, results in lower initiation conditions for geological hazards, increasing the likelihood of their occurrence [16]. Utilizing the surface analysis function of ArcGIS 10.2 software, the slope aspect information of the study area was extracted from the DEM data, and it was divided into north, northeast, east, southeast, south, southwest, west, and northwest (Figure 2c).
- (4)
- Normalized difference vegetation index (NDVI): Vegetation serves the functions of slope protection, stabilization, and soil conservation, which contribute to slope stability [17]. In general, areas characterized by high vegetation coverage tend to experience less severe development of geological hazards. The robust root systems of vegetation exert substantial tension on the slope, effectively anchoring it and increasing its resistance to the formation of sliding zones caused by the infiltration of rainwater. In this study, the normalized vegetation index in the study area is divided into five categories: <0, 0–0.2, 0.2–0.4, 0.4–0.6, and >0.6 (Figure 2d).
- (5)
- Lithology: Lithology reflects the physical and chemical properties of the minerals that compose the rock mass. During the evaluation of geological hazard susceptibility, the chemical properties primarily manifest through the chemical reactions occurring between minerals within the rock mass and other factors such as water, atmospheric rainfall, fertilizers, and so on. These reactions lead to a reduction in the original strength of the rock mass. The physical properties are more evident in the structure, mechanical properties, and engineering geological properties of the mineral itself. The lithology of the study area is divided into four categories: loose soil, soft rock, soft and hard interbedded rock, and medium hard rock (Figure 2e).
- (6)
- The proximity to faults: Geological hazards tend to transpire in regions with active fault structures, and a close correlation exists between the two. Specifically, in the intersecting zones of regional fault structures, the rock tends to be more fragmented, creating a structural environment conducive to the formation and progression of geological hazards [18]. Based on the 1:50,000 geological map of the study area, fault belt information is extracted using the ArcGIS platform, and a 500 m interval buffer zone is established. The study area’s distance from faults is divided into five categories: <500 m, 500 m–1000 m, 1000 m–1500 m, 1500 m–2000 m, and >2000 m (Figure 2f).
- (7)
- Distance from rivers: Rivers alter surface morphology and constitute a major cause of geological hazards. Rivers exert an erosive effect on the slopes on both sides. Under the cyclic erosion of hydrodynamics, slopes can easily form an empty face, causing the gravity of the upper rock mass to exceed the critical tension it can withstand and thus triggering geological hazards. Based on the distribution characteristics of the water system and geological hazards in the study area, the distance from rivers is divided into six grades: <200 m, 200 m–400 m, 400 m–600 m, 600 m–800 m, 800 m–1000 m, and >1000 m (Figure 2g).
- (8)
- Distance from roads: Roads represent the impact of human engineering activities on rock and soil. During the construction of essential projects, excavating mountains and cutting slopes are inevitable processes. Consequently, the occurrence of vibrations and disturbances can create voids within rock and soil masses, facilitating water infiltration and altering their natural stress state. This modification subsequently reduces cohesion and internal friction angle of these masses. As a result, the sliding body is more prone to surpass the equilibrium state. Based on the vector data of the main roads in the study area, this paper establishes a buffer zone with 300 m as a segment in ArcGIS 10.2 software and divides the distance from roads into six categories of 1500 m (Figure 2h).
4.2. Evaluation Model
4.3. Evaluation Results and Verification
5. Conclusions
- (1)
- This study concentrated on evaluating the susceptibility of slope rock and soil mass movement geological hazards in Xuanwei City. After examining the spatial distribution and development environment characteristics of existing geological hazards in the region, elevation, slope, aspect, NDVI, stratigraphic lithology, distance from faults, distance from rivers, and distance from roads were chosen as evaluation factors. The CF-Logistic evaluation model and I-Logistic evaluation model were employed to divide geological hazard susceptibility in Xuanwei City using the ArcGIS platform.
- (2)
- Based on the two coupling models, it was determined that NDVI, elevation, and distance from faults have a significant influence on geological hazard susceptibility in the study area. Particularly, when NDVI < 0, when the altitude ranges between 1500 and 2000 m, and when the distance from the fault is less than 500 m, the CF value, I value, and logistic regression coefficients of the three factors are relatively large. This implies that there is a high likelihood of slope rock and soil mass movement geological hazards occurring under these conditions. Therefore, areas with such environmental characteristics should be prioritized for significant attention and mitigation measures.
- (3)
- The areas with extremely high susceptibility to slope rock and soil mass movement geological hazards are primarily concentrated along road networks and densely populated regions. These areas exhibit a well-developed geological structure characterized by fragmented rock and significant influence from fault zones. High-prone areas are primarily situated near rivers and fault zones, featuring diverse terrains. Medium-prone areas are notably affected by recent tectonic movements, encompass a relatively well-developed surface water system, and possess complex geological environmental conditions. Low-prone areas are mainly distributed in the central, northeastern, and southwestern regions of Xuanwei City. The slope of these areas is mostly between 5° and 10°, hydrogeological conditions are relatively simple, and the hazard environment is not complicated.
- (4)
- The partition results of the two models are roughly similar in spatial distribution, and differences in the partition area arise due to slight variations in evaluation factors among different models. Overall, the evaluation results of the two models align with the distribution of existing geological hazards in the study area and offer valuable reference for geological hazard risk assessments, hazard prevention, and emergency work. The AUC values of the CF + Logistic model and the I + Logistic model are 0.799 and 0.772, respectively, indicating that both models meet the requirements for objective and scientific evaluations of geological hazards in Xuanwei City. The CF + Logistic model demonstrates higher evaluation performance.
- (5)
- The distribution of hazardous rock mass in the study area is extensive, and engineering treatment would require considerable economic investment. Currently, the most effective measures for prevention and control involve extensive observation and preparedness, coupled with enhanced rainfall monitoring and regulations on large-scale excavation to mitigate the impact of collapse hazards. Landslide hazards profoundly impact the safety of lives and property in the study area, and the collective relocation of affected communities presents both challenges and feasibility. Based on the findings of the susceptibility zoning for slope rock and soil mass movement geological hazards, it is crucial to implement suitable engineering control measures that are tailored to the local conditions. These measures aim to effectively manage areas that are susceptible to landslides. This includes formulating clear prevention and control policies for geological hazards during flood seasons, thereby reducing their impact on landslide hazards. Debris flow hazards mainly occur in deep valleys with steep terrain, fragmented rock, and heavy rainfall, causing severe damage to affected bodies. Planting trees in debris flow formation areas can help reduce soil erosion and control from the roots, employing blocking engineering measures in circulation areas can obstruct debris flow, and constructing sedimentation fields or drainage ditches in accumulation areas can prevent debris flow from impacting villages or blocking rivers.
6. Discussion and Prospect
6.1. Discussion
6.2. Prospect
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Evaluation Factor | Level | I | CF | Evaluation Factor | Level | I | CF |
---|---|---|---|---|---|---|---|
Elevation | <1000 m | 0.00000 | −1.00000 | Lithology | Loose soil | −1.19086 | −0.69605 |
1000 m–1200 m | 0.00000 | −1.00000 | Soft rock | 0.53675 | 0.41537 | ||
1200 m–1500 m | 0.41285 | 0.33825 | Soft and hard interlayer rock | −0.04904 | −0.04786 | ||
1500 m–2000 m | 0.83139 | 0.56458 | Medium-hard rock | −0.67202 | −0.95823 | ||
>2000 m | −0.84333 | −0.56974 | Distance from the fault | <500 m | 0.25879 | 0.22803 | |
Slope | <10° | −0.40665 | −0.33414 | 500 m–1000 m | 0.05017 | 0.04893 | |
10°–30° | 0.20999 | 0.18941 | 1000 m–1500 m | −0.31958 | −0.27356 | ||
30°–50° | −0.44167 | −0.35705 | 1500 m–2000 m | −0.19436 | −0.17665 | ||
>50° | 1.72732 | 0.82228 | >2000 m | −0.39595 | −0.32697 | ||
Gradient | North | 0.14665 | 0.13641 | Distance from the road | <200 m | 0.56208 | 0.43000 |
Northeast | 0.09084 | 0.08684 | 200 m–400 m | 0.79432 | 0.54814 | ||
East | −0.00269 | −0.00269 | 400 m–600 m | 0.65283 | 0.47945 | ||
Southeast | −0.35150 | −0.29638 | 600 m–800 m | 0.70415 | 0.50550 | ||
South | −0.09546 | −0.09105 | 800 m–1000 m | 0.46352 | 0.37095 | ||
Southwest | −0.40369 | −0.99995 | >1000 m | −0.16346 | −0.99994 | ||
West | 0.04312 | −0.99992 | Distance from the river | <300 m | 0.43838 | 0.35494 | |
Northwest | 0.31095 | −0.99990 | 300 m–600 m | 0.10159 | 0.09661 | ||
NDVI | <0 | 1.77359 | 0.83032 | 600 m–900 m | 0.2265 | 0.20269 | |
0–0.2 | 0.13197 | 0.12364 | 900 m–1200 m | −0.20221 | −0.18308 | ||
0.2–0.4 | 0.37646 | 0.31373 | 1200 m–1500 m | −0.16293 | −0.15035 | ||
0.4–0.6 | 0.34863 | 0.29436 | >1500 m | −0.20707 | −0.99994 | ||
>0.6 | −1.51451 | −0.78009 |
Model | Factor | β | Standard Error | Wald | Degree of Freedom | Significance | Exp(B) |
---|---|---|---|---|---|---|---|
CF | NDVI | 1.689 | 0.236 | 51.325 | 1 | 0.000 | 5.415 |
Distance from road | −0.032 | 0.159 | 0.040 | 1 | 0.841 | 0.969 | |
Distance from fault | 0.796 | 0.416 | 3.662 | 1 | 0.056 | 2.217 | |
Elevation | 1.206 | 0.166 | 52.516 | 1 | 0.000 | 3.341 | |
Slope | 0.867 | 0.358 | 5.865 | 1 | 0.015 | 2.379 | |
Gradient | −0.073 | 0.185 | 0.158 | 1 | 0.691 | 0.929 | |
Distance from river | 0.138 | 0.153 | 0.818 | 1 | 0.366 | 1.148 | |
Lithology | 0.701 | 0.242 | 8.418 | 1 | 0.004 | 2.016 | |
Constant | −0.076 | 0.172 | 0.196 | 1 | 0.658 | 0.927 | |
I | NDVI | 1.130 | 0.151 | 55.894 | 1 | 0.000 | 3.096 |
Distance from road | −2.031 | 0.250 | 66.281 | 1 | 0.000 | 0.131 | |
Distance from fault | 1.234 | 0.397 | 9.681 | 1 | 0.002 | 3.436 | |
Elevation | 1.011 | 0.127 | 63.194 | 1 | 0.000 | 2.747 | |
Slope | 0.395 | 0.329 | 1.443 | 1 | 0.230 | 1.485 | |
Gradient | 1.604 | 0.438 | 13.398 | 1 | 0.000 | 4.973 | |
Distance from river | 0.148 | 0.305 | 0.235 | 1 | 0.628 | 1.159 | |
Lithology | 0.598 | 0.260 | 5.290 | 1 | 0.021 | 1.819 | |
Constant | −0.261 | 0.102 | 6.581 | 1 | 0.010 | 0.771 |
Model | Dimension | Eigenvalue | Condition Index | Variance Ratio | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
(Constant) | NDVI | Distance from Road | Distance from Fault | Elevation | Slope | Gradient | Distance from River | Lithology | ||||
CF | 1 | 2.651 | 1.000 | 0.03 | 0.00 | 0.04 | 0.00 | 0.00 | 0.00 | 0.05 | 0.04 | 0.01 |
2 | 1.376 | 1.388 | 0.01 | 0.18 | 0.01 | 0.13 | 0.25 | 0.02 | 0.01 | 0.00 | 0.04 | |
3 | 1.146 | 1.521 | 0.00 | 0.09 | 0.04 | 0.01 | 0.00 | 0.47 | 0.00 | 0.00 | 0.21 | |
4 | 0.963 | 1.659 | 0.00 | 0.22 | 0.00 | 0.49 | 0.00 | 0.04 | 0.00 | 0.00 | 0.22 | |
5 | 0.836 | 1.781 | 0.00 | 0.05 | 0.00 | 0.23 | 0.08 | 0.21 | 0.00 | 0.01 | 0.51 | |
6 | 0.728 | 1.909 | 0.00 | 0.41 | 0.03 | 0.12 | 0.41 | 0.13 | 0.07 | 0.00 | 0.00 | |
7 | 0.642 | 2.032 | 0.00 | 0.00 | 0.58 | 0.00 | 0.13 | 0.12 | 0.21 | 0.01 | 0.00 | |
8 | 0.480 | 2.351 | 0.02 | 0.03 | 0.14 | 0.01 | 0.04 | 0.01 | 0.49 | 0.35 | 0.01 | |
9 | 0.179 | 3.849 | 0.94 | 0.02 | 0.16 | 0.01 | 0.08 | 0.00 | 0.16 | 0.58 | 0.01 | |
I | 1 | 1.520 | 1.000 | 0.01 | 0.08 | 0.01 | 0.08 | 0.20 | 0.03 | 0.00 | 0.13 | 0.04 |
2 | 1.374 | 1.052 | 0.21 | 0.01 | 0.28 | 0.01 | 0.01 | 0.06 | 0.00 | 0.01 | 0.05 | |
3 | 1.137 | 1.156 | 0.13 | 0.10 | 0.02 | 0.08 | 0.01 | 0.22 | 0.19 | 0.00 | 0.08 | |
4 | 1.073 | 1.190 | 0.02 | 0.03 | 0.00 | 0.00 | 0.00 | 0.06 | 0.36 | 0.18 | 0.23 | |
5 | 0.976 | 1.248 | 0.00 | 0.38 | 0.01 | 0.39 | 0.00 | 0.10 | 0.05 | 0.00 | 0.04 | |
6 | 0.815 | 1.366 | 0.01 | 0.22 | 0.05 | 0.01 | 0.00 | 0.28 | 0.05 | 0.16 | 0.37 | |
7 | 0.796 | 1.382 | 0.01 | 0.00 | 0.00 | 0.32 | 0.00 | 0.10 | 0.34 | 0.25 | 0.16 | |
8 | 0.707 | 1.466 | 0.11 | 0.07 | 0.13 | 0.10 | 0.57 | 0.00 | 0.00 | 0.26 | 0.01 | |
9 | 0.602 | 1.590 | 0.49 | 0.11 | 0.50 | 0.00 | 0.20 | 0.14 | 0.00 | 0.02 | 0.01 |
Factor | β | Standard Error | Wald | Degree of Freedom | Significance | Exp(B) | |
---|---|---|---|---|---|---|---|
CF | NDVI | 1.683 | 0.235 | 51.358 | 1 | 0.000 | 5.380 |
Distance from road | −0.032 | 0.158 | 0.041 | 1 | 0.840 | 0.969 | |
Distance from fault | 0.809 | 0.415 | 3.808 | 1 | 0.048 | 2.247 | |
Elevation | 1.237 | 0.163 | 57.672 | 1 | 0.000 | 3.445 | |
Slope | 0.871 | 0.357 | 5.941 | 1 | 0.015 | 2.390 | |
Lithology | 0.690 | 0.241 | 8.213 | 1 | 0.004 | 1.994 | |
Constant | −0.141 | 0.113 | 1.578 | 1 | 0.209 | 0.868 | |
I | NDVI | 1.113 | 0.150 | 54.732 | 1 | 0.000 | 3.044 |
Distance from road | −2.054 | 0.249 | 68.262 | 1 | 0.000 | 0.128 | |
Distance from fault | 1.243 | 0.396 | 9.842 | 1 | 0.002 | 3.464 | |
Elevation | 1.037 | 0.125 | 69.372 | 1 | 0.000 | 2.821 | |
Gradient | 1.627 | 0.436 | 13.918 | 1 | 0.000 | 5.091 | |
Lithology | 0.656 | 0.256 | 6.543 | 1 | 0.011 | 1.926 | |
Constant | −0.271 | 0.101 | 7.184 | 1 | 0.007 | 0.763 |
Susceptibility Division | Evaluation Model | Area/km2 | Area Proportion/% (A) | Hazards/pcs | Proportion of Hazard Points/% (D) | Ratio (R = D/A) |
---|---|---|---|---|---|---|
Extremely high-prone areas | CF+LR | 369.46 | 6.09 | 92 | 27.88 | 4.58 |
I+LR | 89.96 | 1.48 | 11 | 3.33 | 2.25 | |
High-prone areas | CF+LR | 1886.78 | 31.08 | 159 | 48.18 | 1.55 |
I+LR | 3137.75 | 51.69 | 281 | 85.15 | 1.65 | |
Medium-susceptible area | CF+LR | 1958.34 | 32.26 | 69 | 20.91 | 0.65 |
I+LR | 1584.39 | 26.10 | 32 | 9.70 | 0.37 | |
Low-susceptible area | CF+LR | 1855.30 | 30.57 | 10 | 3.03 | 0.10 |
I+LR | 1257.78 | 20.72 | 6 | 1.82 | 0.09 |
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Zhang, S.; Tan, S.; Liu, L.; Ding, D.; Sun, Y.; Li, J. Slope Rock and Soil Mass Movement Geological Hazards Susceptibility Evaluation Using Information Quantity, Deterministic Coefficient, and Logistic Regression Models and Their Comparison at Xuanwei, China. Sustainability 2023, 15, 10466. https://doi.org/10.3390/su151310466
Zhang S, Tan S, Liu L, Ding D, Sun Y, Li J. Slope Rock and Soil Mass Movement Geological Hazards Susceptibility Evaluation Using Information Quantity, Deterministic Coefficient, and Logistic Regression Models and Their Comparison at Xuanwei, China. Sustainability. 2023; 15(13):10466. https://doi.org/10.3390/su151310466
Chicago/Turabian StyleZhang, Shaohan, Shucheng Tan, Lifeng Liu, Duanyu Ding, Yongqi Sun, and Jun Li. 2023. "Slope Rock and Soil Mass Movement Geological Hazards Susceptibility Evaluation Using Information Quantity, Deterministic Coefficient, and Logistic Regression Models and Their Comparison at Xuanwei, China" Sustainability 15, no. 13: 10466. https://doi.org/10.3390/su151310466
APA StyleZhang, S., Tan, S., Liu, L., Ding, D., Sun, Y., & Li, J. (2023). Slope Rock and Soil Mass Movement Geological Hazards Susceptibility Evaluation Using Information Quantity, Deterministic Coefficient, and Logistic Regression Models and Their Comparison at Xuanwei, China. Sustainability, 15(13), 10466. https://doi.org/10.3390/su151310466