Enhancing the Performance of Landslide Susceptibility Mapping with Frequency Ratio and Gaussian Mixture Model
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Data and Sources
2.3. Landslide Causative Factors
3. Methods
3.1. Relationship Analysis between Landslides and Causative Factors
3.1.1. FR Method
3.1.2. IV Method
3.1.3. CF Method
3.2. Machine-Learning Models
3.3. Gaussian Mixture Model
3.4. Verification and Reliability Analysis
3.5. Multicollinearity Diagnosis
4. Results
4.1. Relationship between Landslides and Causative Factors
4.2. Multicollinearity Analysis of Landslide Causative Factors
4.3. Landslide Susceptibility Mapping
4.4. Rationality Analysis of Landslide Susceptibility Mapping
4.5. Performance Assessment of Models
4.6. The Contribution of Causative Factors from Different Models
4.7. Comparison of Landslide Susceptibility Zoning Methods
5. Discussion
5.1. Understanding the Effectiveness of LSM
5.2. General Description of Landslide Susceptibility Zoning Methods
5.3. Rationality Analysis of Grid Resolution
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
Acronyms | Complete names |
LSM | landslide susceptibility mapping |
FR | frequency ratio |
IV | information value |
CF | certainty factor |
RF | random forest |
SVM | support vector machine |
LR | logistic regression |
GMM | Gaussian mixture model |
TS | training set |
VS | validation set |
NDVI | normalized differential vegetation index |
TWI | topographic wetness index |
IDL | interactive data language |
EM | expectation maximization |
OA | overall accuracy |
mIoU | mean intersection over union |
FD | frequency density |
TOL | tolerance |
VIF | variance inflation factor |
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Factor | Subset | FR | IV | CF |
---|---|---|---|---|
Elevation (m) | 81–237 | 1.123 | 0.116 | 0.109 |
237–394 | 0.852 | −0.161 | −0.148 | |
394–550 | 0.723 | −0.324 | −0.279 | |
550–706 | 0.628 | −0.466 | −0.373 | |
706–863 | 0.224 | −1.498 | −0.778 | |
863–1019 | 0 | 0 | −1 | |
Slope (°) | 0–9 | 0.865 | −0.145 | −0.136 |
9–18 | 1.472 | 0.386 | 0.320 | |
18–27 | 0.740 | −0.301 | −0.261 | |
27–36 | 0.337 | −1.087 | −0.662 | |
36–46 | 0.276 | −1.289 | −0.724 | |
46–54.6 | 0 | 0 | −1 | |
Aspect (°) | −1 | 0.613 | −0.489 | −0.386 |
0–22.5; 337.5–360 | 0.852 | −0.070 | −0.148 | |
22.5–67.5 | 0.781 | −0.248 | −0.220 | |
67.5–112.5 | 0.700 | −0.356 | −0.299 | |
112.5–157.5 | 0.887 | −0.120 | −0.115 | |
157.5–202.5 | 1.177 | 0.163 | 0.150 | |
202.5–247.5 | 1.314 | 0.273 | 0.238 | |
247.5–292.5 | 1.307 | 0.268 | 0.235 | |
292.5–337.5 | 1.042 | 0.041 | 0.039 | |
Relief amplitude (°) | 0–20 | 1.091 | 0.087 | 0.082 |
20–40 | 0.846 | −0.167 | −0.156 | |
40–60 | 0.409 | −0.895 | −0.591 | |
60–80 | 0 | 0 | −1 | |
80–100 | 0 | 0 | −1 | |
100–113 | 0 | 0 | −1 | |
TWI | 2.8–7.2 | 1.127 | 0.119 | 0.111 |
7.2–11.6 | 0.751 | −0.287 | −0.251 | |
11.6–16.0 | 0.773 | −0.257 | −0.228 | |
16.0–20.4 | 0.150 | −1.896 | −0.847 | |
20.4–24.8 | 0.299 | −1.206 | −0.701 | |
24.8–29.2 | 0 | 0 | −1 | |
Proximity to roads (m) | 0–30 | 0.323 | −1.132 | 0.790 |
30–60 | 1.696 | 0.528 | 0.708 | |
60–90 | 2.301 | 0.833 | 0.559 | |
90–120 | 4.956 | 1.601 | 0.446 | |
>120 | 6.366 | 1.851 | −0.414 | |
Proximity to rives (m) | 0–30 | 1.389 | 0.329 | 0.280 |
30–60 | 1.638 | 0.493 | 0.389 | |
60–90 | 1.823 | 0.601 | 0.451 | |
90–120 | 1.556 | 0.442 | 0.357 | |
>120 | 0.965 | −0.035 | −0.036 | |
Proximity to faults (m) | 0–30 | 1.031 | 0.030 | 0.030 |
30–60 | 1.312 | 0.272 | 0.238 | |
60–90 | 1.661 | 0.508 | 0.398 | |
90–120 | 1.189 | 0.173 | 0.159 | |
>120 | 0.960 | −0.041 | −0.041 | |
NDVI | −0.376–−0.176 | 0 | 0 | −1 |
−0.176–0.024 | 0 | 0 | −1 | |
0.024–0.223 | 0.341 | −1.075 | −0.660 | |
0.223–0.423 | 1.184 | 0.169 | 0.154 | |
0.423–0.623 | 1.278 | 0.245 | 0.217 | |
0.623–0.823 | 0.371 | −0.993 | −0.629 | |
Land use | Water | 0.612 | −0.492 | −0.389 |
Artificial area | 0.248 | −1.393 | −0.752 | |
Forest land | 0.439 | −0.822 | −0.560 | |
Shrub | 1.969 | 0.677 | 0.492 | |
Farm land | 1.018 | 0.018 | 0.018 | |
Bare land | 0.521 | 0.521 | 0.406 | |
Lithology | Granite | 1.355 | 0.304 | 0.261 |
Other magmatic rock | 0.934 | −0.068 | −0.067 | |
Metamorphic rock | 1.051 | 0.050 | 0.049 | |
Sedimentary rock | 0.951 | −0.050 | −0.049 | |
Carbonate rock | 0 | 0 | −1 | |
Mudstone and shale | 0.821 | −0.197 | −0.179 | |
Average annual precipitation (mm) | 1244.2–1276.8 | 1.146 | 0.136 | 0.125 |
1276.8–1309.4 | 0.793 | −0.232 | −0.207 | |
1309.4–1342.0 | 1.131 | 0.123 | 0.115 | |
1342.0–1374.6 | 0.563 | −0.575 | −0.437 | |
1374.6–1407.2 | 0.495 | −0.702 | −0.506 | |
1407.2–1439.8 | 1.436 | 0.362 | 0.303 |
Variable | TOL | VIF |
---|---|---|
Elevation | 0.660 | 1.516 |
Slope | 0.693 | 1.443 |
Aspect | 0.977 | 1.024 |
Relief amplitude | 0.559 | 1.788 |
TWI | 0.876 | 1.142 |
Proximity to roads | 0.774 | 1.292 |
Proximity to rives | 0.986 | 1.014 |
Proximity to faults | 0.978 | 1.022 |
NDVI | 0.595 | 1.681 |
Land use | 0.737 | 1.356 |
Lithology | 0.776 | 1.288 |
Average annual precipitation | 0.954 | 1.048 |
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Huangfu, W.; Qiu, H.; Wu, W.; Qin, Y.; Zhou, X.; Zhang, Y.; Ullah, M.; He, Y. Enhancing the Performance of Landslide Susceptibility Mapping with Frequency Ratio and Gaussian Mixture Model. Land 2024, 13, 1039. https://doi.org/10.3390/land13071039
Huangfu W, Qiu H, Wu W, Qin Y, Zhou X, Zhang Y, Ullah M, He Y. Enhancing the Performance of Landslide Susceptibility Mapping with Frequency Ratio and Gaussian Mixture Model. Land. 2024; 13(7):1039. https://doi.org/10.3390/land13071039
Chicago/Turabian StyleHuangfu, Wenchao, Haijun Qiu, Weicheng Wu, Yaozu Qin, Xiaoting Zhou, Yang Zhang, Mohib Ullah, and Yanfen He. 2024. "Enhancing the Performance of Landslide Susceptibility Mapping with Frequency Ratio and Gaussian Mixture Model" Land 13, no. 7: 1039. https://doi.org/10.3390/land13071039
APA StyleHuangfu, W., Qiu, H., Wu, W., Qin, Y., Zhou, X., Zhang, Y., Ullah, M., & He, Y. (2024). Enhancing the Performance of Landslide Susceptibility Mapping with Frequency Ratio and Gaussian Mixture Model. Land, 13(7), 1039. https://doi.org/10.3390/land13071039