Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping
<p>Location of landslides in the study area in Kurdistan Province of Iran.</p> "> Figure 2
<p>Lithological map of the study area.</p> "> Figure 3
<p>Flowchart of modeling process and methodology used in this study.</p> "> Figure 4
<p>The effects of sample size and raster resolution on the performance of landslide modeling: (<b>a</b>) sample size of 60%/40%; (<b>b</b>) sample size of 70%/30%; (<b>c</b>) sample size of 80%/20%; and (<b>d</b>) sample size of 90%/10%.</p> "> Figure 4 Cont.
<p>The effects of sample size and raster resolution on the performance of landslide modeling: (<b>a</b>) sample size of 60%/40%; (<b>b</b>) sample size of 70%/30%; (<b>c</b>) sample size of 80%/20%; and (<b>d</b>) sample size of 90%/10%.</p> "> Figure 5
<p>The trend of changes of the number of seed and iteration in the landslide modeling process: (<b>a</b>) optimum number of iteration for the combination of 60%/40% with the raster resolution of 10 m, (<b>b</b>) optimum number of seed for the combination of 60%/40% with the raster resolution of 10 m; (<b>c</b>) optimum number of iteration for the combination of 70%/30% with the raster resolution of 10 m, (<b>d</b>) optimum number of seed for the combination of 70%/30% with the raster resolution of 10 m; (<b>e</b>) optimum number of iteration for the combination of 80%/20% with the raster resolution of 20 m, (<b>f</b>) optimum number of seed for the combination of 80%/20% with the raster resolution of 20 m; (<b>g</b>) optimum number of iteration for the combination of 90%/10% with the raster resolution of 20 m, (<b>h</b>) optimum number of seed for the combination of 90%/10% with the raster resolution of 20 m.</p> "> Figure 5 Cont.
<p>The trend of changes of the number of seed and iteration in the landslide modeling process: (<b>a</b>) optimum number of iteration for the combination of 60%/40% with the raster resolution of 10 m, (<b>b</b>) optimum number of seed for the combination of 60%/40% with the raster resolution of 10 m; (<b>c</b>) optimum number of iteration for the combination of 70%/30% with the raster resolution of 10 m, (<b>d</b>) optimum number of seed for the combination of 70%/30% with the raster resolution of 10 m; (<b>e</b>) optimum number of iteration for the combination of 80%/20% with the raster resolution of 20 m, (<b>f</b>) optimum number of seed for the combination of 80%/20% with the raster resolution of 20 m; (<b>g</b>) optimum number of iteration for the combination of 90%/10% with the raster resolution of 20 m, (<b>h</b>) optimum number of seed for the combination of 90%/10% with the raster resolution of 20 m.</p> "> Figure 5 Cont.
<p>The trend of changes of the number of seed and iteration in the landslide modeling process: (<b>a</b>) optimum number of iteration for the combination of 60%/40% with the raster resolution of 10 m, (<b>b</b>) optimum number of seed for the combination of 60%/40% with the raster resolution of 10 m; (<b>c</b>) optimum number of iteration for the combination of 70%/30% with the raster resolution of 10 m, (<b>d</b>) optimum number of seed for the combination of 70%/30% with the raster resolution of 10 m; (<b>e</b>) optimum number of iteration for the combination of 80%/20% with the raster resolution of 20 m, (<b>f</b>) optimum number of seed for the combination of 80%/20% with the raster resolution of 20 m; (<b>g</b>) optimum number of iteration for the combination of 90%/10% with the raster resolution of 20 m, (<b>h</b>) optimum number of seed for the combination of 90%/10% with the raster resolution of 20 m.</p> "> Figure 6
<p>Landslide susceptibility mapping prepared by the ADTree model and its ensemble: (<b>a</b>) ADTree, sample size 60/40 & Resolution: 10 m; (<b>b</b>) RS-ADT, sample size 60/40 & Resolution: 10 m; (<b>c</b>) ADTree, sample size 70/30 & Resolution: 10 m; (<b>d</b>) RS-ADTree, sample size 70/30 & Resolution: 10 m; (<b>e</b>) ADTree, sample size 80/20 & Resolution: 20 m; (<b>f</b>) MB-ADTree, sample size 80/20 & Resolution: 20 m; (<b>g</b>) ADTree, sample size 90/10 & Resolution: 20 m; (<b>h</b>) MB-ADTree, sample size 90/10 & Resolution: 20 m.</p> "> Figure 6 Cont.
<p>Landslide susceptibility mapping prepared by the ADTree model and its ensemble: (<b>a</b>) ADTree, sample size 60/40 & Resolution: 10 m; (<b>b</b>) RS-ADT, sample size 60/40 & Resolution: 10 m; (<b>c</b>) ADTree, sample size 70/30 & Resolution: 10 m; (<b>d</b>) RS-ADTree, sample size 70/30 & Resolution: 10 m; (<b>e</b>) ADTree, sample size 80/20 & Resolution: 20 m; (<b>f</b>) MB-ADTree, sample size 80/20 & Resolution: 20 m; (<b>g</b>) ADTree, sample size 90/10 & Resolution: 20 m; (<b>h</b>) MB-ADTree, sample size 90/10 & Resolution: 20 m.</p> "> Figure 7
<p>Model comparison and evaluation of the ADTree and its ensembles in different sample sizes and raster resolutions using: training dataset (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>); and validation dataset (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>).</p> "> Figure 7 Cont.
<p>Model comparison and evaluation of the ADTree and its ensembles in different sample sizes and raster resolutions using: training dataset (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>); and validation dataset (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>).</p> ">
Abstract
:1. Introduction
2. Description of Study Area
3. Data Acquisition and Processing
3.1. Landslide Inventory Map
3.2. Landslide Conditioning Factors
4. Methodology
4.1. Alternating Decision Tree (ADTree)
4.2. Bagging (BA)
4.3. Multiboost (MB)
4.4. Random Subspace (RS)
4.5. Rotation Forest (RF)
4.6. Comparison and Validation Techniques
4.6.1. Statistical Index-Based Measures
4.6.2. Receiver Operating Characteristic Curve
4.6.3. Parametric and Non-Parametric Statistical Tests
4.7. Factor Selecting based on the Information Gain Ration (IGR) Technique
5. Result and Analysis
5.1. Important Factors for Landslide Modeling
5.2. Selecting the Best Raster Resolution for Each Combination
5.3. Landslide Modeling Process
5.4. Landslide Susceptibility Mapping
5.5. Evaluation of Landslide Susceptibility Maps
6. Discussion
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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No. | Landslide Causal Factors | Classes | |
---|---|---|---|
Topographic factors | 1 | Slope (o) | (1) 0–5; (2) 5–10; (3) 10–15; (4) 15–20; (5) 20–25; (6) 25–30; (7) 30–45; (8) >45 |
2 | Aspect | (1) Flat; (2) North; (3) Northeast; (4) East; (5) Southeast; (6) South; (7) Southwest; (8) West; (9) Northwest | |
3 | Elevation (m) | (1) 1573–1700; (2) 1700–1800; (3) 1800–1900; (4) 1900–2000; (5) 2000–2100; (6) 2100–2200; (7) 2200–2300; (8) 2300–2400; (9) >2400 | |
4 | Curvature (m−1) | (1) [(−12.5)–(−1.4)]; (2) [(−1.4)–(−0.4)]; (3) [(−0.4)–(−0.2)]; (4) [(−0.2)–0.9]; (5) [0.9–2.5]; (6) [2.5–15.6] | |
5 | Plan curvature (m−1) | (1) [(−6.7)–(−0.8)]; (2) [(−0.8)–(−0.2)]; (3) [(−0.2)–0]; (4) [0–0.4]; (5) [0.4–1.1]; (6) [1.1–10.4] | |
6 | Profile curvature (m−1) | (1) [(−10.7)–(−1.7)]; (2) [(−1.7)–(−0.7)]; (3) [(−0.7)–(−0.2)]; (4) [(−0.2)–0.2]; (5) [0.2–0.9]; (6) [0.9–7.5] | |
7 | STI | (1) 0–7; (2) 7–14; (3) 14–21; (4) 21–28; (5) 28–35; (6) 35–42 | |
Hydrological factors | 8 | Rainfall (mm) | (1) 263–270; (2) 270–300; (3) 300–330; (4) 330–360; (5) 360–390; (6) 390–420; (7) 420–450 |
9 | Annual solar radiation (h) | (1) 3.015–6.563; (2) 5.563–6.747; (3) 6.747–6.849; (4) 6.849–6.930; (5) 6.930–7.073; (6) 7.073–7.236; (7) 7.236–8.215 | |
10 | SPI | (1) 0–998; (2) 998–6986; (3) 6986–19,961; (4) 19,961–45,911; (5) 45,911–101,803; (6) 101,803–255,505 | |
11 | TWI | (1) 1–3; (2) 3–4; (3) 4–6; (4) 6–8; (5) 8–9; (6) 9–11 | |
12 | Distance to Rivers (m) | (1) 0–50; (2) 50–100; (3) 100–150; (4) 150–200; (5) >200 | |
13 | River density (km/km2) | (1) 0–1.9; (2) 1.9–3.2; (3) 3.2–4.2; (4) 4.2–5.2; (5) 5.2–6.3; (6) 6.3–7.8; (7) 7.8–13.2 | |
Lithological factors | 14 | Lithology | (1) Quaternary (2) Tertiary (3) Cretaceous |
15 | Distance to Faults (m) | (1) 0–200; (2) 200–400; (3) 400–600; (4) 600–800; (5) 800–1000; (6) >1000 | |
16 | Fault density (km/km2) | (1) 0–0.3; (2) 0.3–0.8; (3) 0.8–1.2; (4) 1.2–1.7; (5) 1.7–2.1; (6) 2.1–2.5; (7) 2.5–3.2 | |
Land Cover Factors | 17 | Land use | (1) Residential area (2) Arable land (dry faring and cultivated lands); (3) Wood land; (4) Grassland; (5) Barren land |
18 | NDVI | (1) [(−0.23)–(−0.061)]; (2) [(−0.061)–(−0.0081)]; (3) [(−0.0081)–(0.060)]; (4) [(0.060)–0.14]; (5) [0.14–0.24]; (6) [0.24–0.41]; (7) [0.41–0.73] | |
Anthropogenic factors | 19 | Distance to Roads (m) | (1) 0–50; (2) 50–100; (3) 100–150; (4) 150–200; (5) >200 |
20 | Road density (km/km2) | (1) 0–0.0013; (2) 0.0013–0.0027; (3) 0.0027–0.0041; (4) 0.0041–0.0055; (5) 0.0055–0.0069; (6) 0.0069–0.0083; (7) 0.0083–0.0097 |
Conditioning Factors | 10 m | 20 m | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
60%/40% | 70%/30% | 80%/20% | 90%/10% | 60%/40% | 70%/30% | 80%/20% | 90%/10% | |||||||||
AM | R | AM | R | AM | R | AM | R | AM | R | AM | R | AM | R | AM | R | |
Slope angle | 0.105 | 2 | 0.482 | 1 | 0.509 | 1 | 0.484 | 1 | 0.135 | 2 | 0.655 | 1 | 0.459 | 1 | 0.481 | 1 |
TWI | 0.142 | 1 | 0.597 | 2 | 0.427 | 2 | 0.409 | 2 | 0.142 | 1 | 0.482 | 2 | 0.428 | 2 | 0.409 | 2 |
Aspect | 0.071 | 3 | 0.065 | 10 | 0.058 | 11 | 0.088 | 6 | 0.071 | 3 | 0.072 | 9 | 0.065 | 7 | 0.085 | 6 |
STI | 0.064 | 4 | 0.195 | 4 | 0.172 | 4 | 0.186 | 4 | 0.064 | 4 | 0.195 | 4 | 0.173 | 3 | 0.186 | 3 |
Profile curvature | 0.005 | 5 | 0.042 | 12 | 0.094 | 7 | 0.031 | 12 | 0 | - | 0.032 | 12 | 0.011 | 12 | 0 | - |
Plan curvature | 0 | - | 0.221 | 3 | 0.174 | 3 | 0.191 | 3 | 0 | - | 0.440 | 3 | 0.172 | 4 | 0.167 | 4 |
Elevation | 0 | - | 0.096 | 7 | 0.086 | 8 | 0.095 | 5 | 0 | - | 0.096 | 7 | 0.059 | 9 | 0.095 | 5 |
Curvature | 0 | - | 0.114 | 5 | 0.106 | 5 | 0.085 | 7 | 0 | - | 0.065 | 10 | 0.022 | 11 | 0.046 | 11 |
Land use | 0 | - | 0.064 | 9 | 0.058 | 11 | 0.050 | 11 | 0 | - | 0.080 | 8 | 0.058 | 10 | 0.070 | 8 |
Rainfall | 0 | - | 0.051 | 11 | 0.064 | 10 | 0.057 | 10 | 0 | - | 0.051 | 11 | 0.065 | 8 | 0.057 | 10 |
SPI | 0 | - | 0.070 | 8 | 0.075 | 9 | 0.071 | 9 | 0 | - | 0.116 | 6 | 0.076 | 6 | 0.071 | 9 |
Solar radiation | 0 | - | 0.099 | 6 | 0.092 | 6 | 0.081 | 8 | 0 | - | 0.119 | 5 | 0.077 | 5 | 0.076 | 7 |
Raster Resolution (m) | 10 | 20 | ||||||
---|---|---|---|---|---|---|---|---|
Sample Size (%) | 60%/40% | 70%/30% | 80%/20% | 90%/10% | 60%/40% | 70%/30% | 80%/20% | 90%/10% |
Statistic Measures | ||||||||
TP | 60 | 77 | 85 | 91 | 55 | 72 | 81 | 89 |
TN | 47 | 81 | 76 | 89 | 48 | 78 | 82 | 92 |
FP | 7 | 0 | 4 | 9 | 12 | 9 | 8 | 11 |
FN | 20 | 4 | 13 | 11 | 19 | 3 | 7 | 8 |
SST % | 0.750 | 0.951 | 0.867 | 0.892 | 0.743 | 0.960 | 0.920 | 0.918 |
SPF % | 0.870 | 1.000 | 0.950 | 0.908 | 0.800 | 0.897 | 0.911 | 0.893 |
ACC % | 0.799 | 0.975 | 0.904 | 0.900 | 0.769 | 0.926 | 0.916 | 0.905 |
Kappa | 0.597 | 0.950 | 0.809 | 0.800 | 0.537 | 0.851 | 0.831 | 0.810 |
RMSE | 0.351 | 0.157 | 0.291 | 0.300 | 0.407 | 0.239 | 0.273 | 0.298 |
Raster Resolution (m) | 10 | 20 | ||||||
---|---|---|---|---|---|---|---|---|
Sample Size (%) | 60%/40% | 70%/30% | 80%/20% | 90%/10% | 60%/40% | 70%/30% | 80%/20% | 90%/10% |
Statistic Measures | ||||||||
TP | 26 | 27 | 17 | 10 | 19 | 27 | 18 | 10 |
TN | 37 | 23 | 19 | 10 | 25 | 22 | 20 | 10 |
FP | 18 | 3 | 5 | 1 | 35 | 3 | 4 | 1 |
FN | 7 | 7 | 3 | 1 | 9 | 8 | 1 | 1 |
SST % | 0.788 | 0.794 | 0.850 | 0.909 | 0.679 | 0.771 | 0.947 | 0.909 |
SPF % | 0.673 | 0.885 | 0.792 | 0.909 | 0.417 | 0.880 | 0.833 | 0.909 |
ACC % | 0.716 | 0.833 | 0.818 | 0.909 | 0.500 | 0.817 | 0.884 | 0.909 |
Kappa | 0.631 | 0.666 | 0.636 | 0.818 | 0.572 | 0.633 | 0.727 | 0.818 |
RMSE | 0.363 | 0.182 | 0.390 | 0.331 | 0.484 | 0.256 | 0.342 | 0.309 |
Ensemble Models | 90%/10% and Resolution 20 m | 80%/20% and Resolution 20 m | 70%/30% and Resolution 10 m | 60/410% and Resolution 10 m | ||||
---|---|---|---|---|---|---|---|---|
S | I | S | I | S | I | S | I | |
MB | 7 | 15 | 5 | 11 | 3 | 10 | 1 | 14 |
BA | 3 | 10 | 4 | 10 | 6 | 10 | 8 | 10 |
RS | 4 | 10 | 8 | 10 | 1 | 11 | 7 | 16 |
RF | 6 | 15 | 3 | 13 | 5 | 13 | 1 | 14 |
Criteria | ADTree | RF | RS | BA | MB | |||||
---|---|---|---|---|---|---|---|---|---|---|
T | V | T | V | T | V | T | V | T | V | |
True positive | 60 | 26 | 46 | 26 | 60 | 30 | 48 | 27 | 52 | 29 |
True negative | 47 | 37 | 61 | 36 | 63 | 36 | 59 | 37 | 63 | 33 |
False positive | 7 | 18 | 21 | 18 | 7 | 14 | 19 | 17 | 15 | 15 |
False negative | 20 | 7 | 6 | 8 | 4 | 8 | 8 | 7 | 4 | 11 |
Sensitivity | 0.750 | 0.788 | 0.885 | 0.765 | 0.938 | 0.789 | 0.857 | 0.794 | 0.929 | 0.725 |
Specificity | 0.870 | 0.673 | 0.744 | 0.667 | 0.900 | 0.720 | 0.756 | 0.685 | 0.808 | 0.688 |
Accuracy | 0.799 | 0.716 | 0.799 | 0.705 | 0.918 | 0.750 | 0.799 | 0.727 | 0.858 | 0.705 |
AUROC | 0.864 | 0.737 | 0.907 | 0.796 | 0.974 | 0.791 | 0.889 | 0.788 | 0.940 | 0.756 |
Criteria | ADTree | RF | RS | BA | MB | |||||
---|---|---|---|---|---|---|---|---|---|---|
T | V | T | V | T | V | T | V | T | V | |
True positive | 77 | 27 | 73 | 28 | 76 | 28 | 75 | 28 | 80 | 28 |
True negative | 81 | 23 | 77 | 22 | 79 | 21 | 78 | 21 | 78 | 22 |
False positive | 0 | 3 | 8 | 2 | 5 | 2 | 6 | 2 | 1 | 8 |
False negative | 4 | 7 | 4 | 8 | 2 | 9 | 3 | 9 | 3 | 2 |
Sensitivity | 0.951 | 0.794 | 0.948 | 0.778 | 0.974 | 0.757 | 0.962 | 0.757 | 0.964 | 0.933 |
Specificity | 1.000 | 0.885 | 0.906 | 0.917 | 0.940 | 0.913 | 1.000 | 0.913 | 0.987 | 0.733 |
Accuracy | 0.975 | 0.833 | 0.926 | 0.833 | 0.957 | 0.817 | 0.981 | 0.817 | 0.975 | 0.833 |
AUROC | 0.979 | 0.862 | 0.984 | 0.898 | 0.997 | 0.901 | 0.983 | 0.893 | 0.996 | 0.892 |
Criteria | ADTree | RF | RS | BA | MB | |||||
---|---|---|---|---|---|---|---|---|---|---|
T | V | T | V | T | V | T | V | T | V | |
True positive | 81 | 18 | 81 | 19 | 78 | 18 | 81 | 18 | 81 | 18 |
True negative | 82 | 20 | 82 | 20 | 82 | 20 | 82 | 20 | 82 | 20 |
False positive | 8 | 4 | 8 | 3 | 11 | 4 | 8 | 4 | 8 | 4 |
False negative | 7 | 1 | 7 | 2 | 7 | 2 | 7 | 2 | 7 | 2 |
Sensitivity | 0.920 | 0.947 | 0.920 | 0.905 | 0.918 | 0.900 | 0.920 | 0.900 | 0.920 | 0.900 |
Specificity | 0.911 | 0.833 | 0.911 | 0.870 | 0.882 | 0.833 | 0.911 | 0.833 | 0.911 | 0.833 |
Accuracy | 0.916 | 0.884 | 0.916 | 0.886 | 0.899 | 0.864 | 0.916 | 0.864 | 0.916 | 0.864 |
AUROC | 0.967 | 0.903 | 0.987 | 0.937 | 0.972 | 0.926 | 0.974 | 0.926 | 0.988 | 0.934 |
Criteria | ADTree | RF | RS | BA | MB | |||||
---|---|---|---|---|---|---|---|---|---|---|
T | V | T | V | T | V | T | V | T | V | |
True positive | 89 | 10 | 96 | 10 | 88 | 10 | 87 | 10 | 92 | 10 |
True negative | 92 | 10 | 94 | 10 | 92 | 9 | 93 | 10 | 95 | 10 |
False positive | 11 | 1 | 4 | 1 | 12 | 1 | 13 | 1 | 8 | 1 |
False negative | 8 | 1 | 6 | 1 | 8 | 2 | 7 | 1 | 5 | 1 |
Sensitivity | 0.918 | 0.909 | 0.941 | 0.909 | 0.917 | 0.833 | 0.926 | 0.909 | 0.948 | 0.909 |
Specificity | 0.893 | 0.909 | 0.959 | 0.909 | 0.885 | 0.900 | 0.877 | 0.909 | 0.922 | 0.909 |
Accuracy | 0.905 | 0.909 | 0.950 | 0.909 | 0.900 | 0.864 | 0.900 | 0.909 | 0.935 | 0.909 |
AUROC | 0.957 | 0.876 | 0.983 | 0.913 | 0.968 | 0.884 | 0.968 | 0.921 | 0.992 | 0.926 |
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Shirzadi, A.; Soliamani, K.; Habibnejhad, M.; Kavian, A.; Chapi, K.; Shahabi, H.; Chen, W.; Khosravi, K.; Thai Pham, B.; Pradhan, B.; et al. Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping. Sensors 2018, 18, 3777. https://doi.org/10.3390/s18113777
Shirzadi A, Soliamani K, Habibnejhad M, Kavian A, Chapi K, Shahabi H, Chen W, Khosravi K, Thai Pham B, Pradhan B, et al. Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping. Sensors. 2018; 18(11):3777. https://doi.org/10.3390/s18113777
Chicago/Turabian StyleShirzadi, Ataollah, Karim Soliamani, Mahmood Habibnejhad, Ataollah Kavian, Kamran Chapi, Himan Shahabi, Wei Chen, Khabat Khosravi, Binh Thai Pham, Biswajeet Pradhan, and et al. 2018. "Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping" Sensors 18, no. 11: 3777. https://doi.org/10.3390/s18113777
APA StyleShirzadi, A., Soliamani, K., Habibnejhad, M., Kavian, A., Chapi, K., Shahabi, H., Chen, W., Khosravi, K., Thai Pham, B., Pradhan, B., Ahmad, A., Bin Ahmad, B., & Tien Bui, D. (2018). Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping. Sensors, 18(11), 3777. https://doi.org/10.3390/s18113777