Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm
"> Figure 1
<p>Location of the study area in Iran; the red circles denote landslides for testing; the red triangles denote landslides for training; the green circles denote non-landslides for testing; and the green triangles denote non-landslides for training.</p> "> Figure 2
<p>Some recent landslides in the Sarkhoon watershed.</p> "> Figure 3
<p>The overall flowchart of landslide susceptibility modeling in the Sarkhoon watershed.</p> "> Figure 4
<p>Factor selection using least square support vector machine (LSSM).</p> "> Figure 5
<p>Landslide susceptibility maps using: (<b>a</b>) AdaBoost-scholastic gradient descent (ABSGD); (<b>b</b>) stochastic gradient descent (SGD); (<b>c</b>) logistic regression (LR); (<b>d</b>) logistic model tree (LMT); and (<b>e</b>) functional tree (FT).</p> "> Figure 5 Cont.
<p>Landslide susceptibility maps using: (<b>a</b>) AdaBoost-scholastic gradient descent (ABSGD); (<b>b</b>) stochastic gradient descent (SGD); (<b>c</b>) logistic regression (LR); (<b>d</b>) logistic model tree (LMT); and (<b>e</b>) functional tree (FT).</p> "> Figure 6
<p>Area under the ROC curve (AUCs) of the models using: (<b>a</b>) training dataset and (<b>b</b>) validation dataset.</p> "> Figure 7
<p>Landslide model validation and comparison using: (<b>a</b>) success rate curve and (<b>b</b>) prediction rate curve.</p> ">
Abstract
:1. Introduction
2. Study Area
3. Methodology
3.1. Landslide Inventory Map (LIM)
3.2. Landslide Conditioning Factors
3.3. AdaBoost Meta Classifier
3.4. Stochastic Gradient Descent Algorithm
3.5. Logistic Regression
3.6. Logistic Model Tree
3.7. Functional Tree
3.8. Factor Selection Using the Least Square Support Vector Machine (LSSVM)
3.9. Evaluation and Comparison of Algorithms
3.9.1. Statistical Index-Based Evaluation
3.9.2. AUC
4. Results and Analysis
4.1. The Most Significant Conditioning Factors in the Modeling Process
4.2. Model Validation and Comparison
4.3. Landslide Susceptibility Mapping
4.4. Map Verification and Comparison
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Factors | Classes | GIS Data Type | Scale | Classification Method |
---|---|---|---|---|
Land use | (1) Dry farming; (2) Sparse forest; (3) Dense forest; (4) Poor rangeland; (5) Good rangeland; (6) Residential area; (7) Rock outcrop | Polygon | 1:25,000 | Supervised classification |
Lithology * | (1) Mmm; (2) MPlsma; (3) PlCb; (4) Qal; (5) Q2t; (6) Q3t; (7) Edj; (8) Klt; (9) Kmg; (10) KlSi | Polygon | 1: 100,000 | Lithological classification |
Average annual precipitation (mm) | (1) 523–650; (2) 650–800 (3) 800–950; (4) 950–1100; (5) 1100–1250; (6) 1250< | GRID | 10 m × 10 m | Natural breaks |
Altitude (m) | (1) 1370–1620; (2) 1620–1870; (3) 1870–2120; (4) 2120–2370; (5) 2370–2620; (6) 2620–2870; (7) 2870–3120; (8) 3120–3375 | GRID | 10 m × 10 m | Natural breaks |
Slope angle (˚) | (1) 0–5; (2) 5–10; (3) 10–15; (4) 15–20; (5) 20–30; (6) 30–45; (7) 45< | GRID | 10 m × 10 m | Manual |
Aspect (˚) | (1) −1–0; (2) 0–22.5, 337.5–360; (3) 22.5–67.5; (4) 67.5–112.5; (5) 112.5–157.5; (6) 157.5–202.5; (7) 202.5–247.5; (8) 247.5–292.5; (9) 292.5–337.5 | GRID | 10 m × 10 m | Azimuth classification |
LS | (1) <−70: (2) −70–−45; (3) −45–−15; (4) −15–15; (5) 15–45; (6) 45< | GRID | 10 m × 10 m | Natural breaks |
General curvature | (1) <−0.1; (2) −0.1–−0.05; (3) −0.05–0; (4) 0–0.05; (5) 0.05< | GRID | 10 m × 10 m | Natural breaks |
Profile curvature | (1) −1.369- −0.084; (2) −0.084–−0.008; (3) −0.008–0.26 | GRID | 10 m × 10 m | Natural breaks |
Plan curvature | (1) −49.714–−0.0119; (2) −0.0119–0.0008; (3) 0.0008–0.0143; (4) 0.0143–8.3923 | GRID | 10 m × 10 m | Natural breaks |
Longitudinal curvature | (1) <−0.1; (2) −0.1–−0.05; (3) −0.05–0; (4) 0–0.05; (5) 0.05–1; (6) 0.1–1.37 | GRID | 10 m × 10 m | Natural breaks |
Tangential curvature | (1) −1.21–−0.051; (2) −1.21–−0.004; (3) −0.004–0.28 | GRID | 10 m × 10 m | Natural breaks |
Solar radiation | (1) <350,000; (2) 350,000–700,000 (3) 700,000–1,050,000; (4) 1,050,000–1,400,000; (5) 1,400,000–1,750,000; (6) 1,750,000< | GRID | 10 m × 10 m | Natural breaks |
SPI | (1) 4–6; (2) 6–8; (3) 8–10; (4) 10–12; (5) 12–14; (6) 14–16; (7) 16–18; (8) 18–20 | GRID | 10 m × 10 m | Natural breaks |
TPI | (1) <−30; (2) −30–−15; (3) −15–0; (4) 0–15; (5) 15–30; (6) 30< | GRID | 10 m × 10 m | Natural breaks |
TWI | (1) 4.71–6.69; (2) 6.69–8.67; (3) 8.67–10.56; (4) 10.56–12.64; (5) 12.64–14.62; (6) 14.62–16.60; (7) 16.60–18.58; (8) 18.58–20.56 | GRID | 10 m × 10 m | Natural breaks |
TRI | (1) <5; (2) 5–15; (3) 15–25; (4) 25–35; (5) 35–45; (6) 45< | GRID | 10 m × 10 m | Natural breaks |
Distance to stream (m) | (1) 0–100; (2) 100–200; (3)200–300; (4) 300–400; (5) 400–500, (6) 500< | Line | 1:25,000 | Manual |
Distance to road (m) | (1) 0–100; (2) 100–200; (3)200–300; (4) 300–400; (5) 400–500, (6) 500< | Line | 1:25,000 | Manual |
Distance to fault (m) | (1) 0–100; (2) 100–200; (3)200–300; (4) 300–400; (5) 400–500, (6) 500< | Line | 1: 100,000 | Manual |
Predicted class | Actual class | ||
Landslide (1) | Non-landslide (0) | ||
Landslide (1) | TP | FP | |
Non-landslide (0) | FN | TN |
ABSGD | SGD | LR | LMT | FT | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
T | V | T | V | T | V | T | V | T | V | ||
TP | 61 | 20 | 56 | 19 | 58 | 21 | 54 | 18 | 52 | 18 | |
TN | 57 | 25 | 58 | 23 | 60 | 22 | 59 | 25 | 59 | 21 | |
FP | 8 | 9 | 13 | 10 | 11 | 8 | 12 | 12 | 10 | 11 | |
FN | 12 | 4 | 11 | 6 | 9 | 7 | 15 | 4 | 17 | 8 | |
SST | 0.836 | 0.833 | 0.836 | 0.760 | 0.866 | 0.750 | 0.783 | 0.818 | 0.754 | 0.692 | |
SPC | 0.877 | 0.735 | 0.817 | 0.697 | 0.845 | 0.733 | 0.831 | 0.676 | 0.855 | 0.656 | |
ACC | 0.855 | 0.776 | 0.826 | 0.724 | 0.855 | 0.741 | 0.807 | 0.729 | 0.804 | 0.672 | |
RMSE | 0.323 | 0.411 | 0.446 | 0.531 | 0.338 | 0.443 | 0.451 | 0.536 | 0.502 | 0.540 | |
AUC | 0.941 | 0.861 | 0.904 | 0.830 | 0.917 | 0.839 | 0.871 | 0.731 | 0.819 | 0.708 |
Algorithm | Parameters |
---|---|
SGD | Bach size, 100; Debug, False; Do not check capability, False; Not normalized, true; Do not replace missing, False; Epoch, 500; Epsilon, 0.001; Lambda, 0.0001; Learning rate, 0.01, Loss function, Logistic regression; Number of decimal places, 2; Number of seeds, 1. |
ABSGD | Batch size, 100; Classifier, SGD; Debug, False; Do not check capability, False; Number of decimal places, 2; Number of iterations, 10; Number of seeds, 1; Use resampling, False; Weight threshold, 100. |
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Share and Cite
Tien Bui, D.; Shahabi, H.; Omidvar, E.; Shirzadi, A.; Geertsema, M.; Clague, J.J.; Khosravi, K.; Pradhan, B.; Pham, B.T.; Chapi, K.; et al. Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm. Remote Sens. 2019, 11, 931. https://doi.org/10.3390/rs11080931
Tien Bui D, Shahabi H, Omidvar E, Shirzadi A, Geertsema M, Clague JJ, Khosravi K, Pradhan B, Pham BT, Chapi K, et al. Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm. Remote Sensing. 2019; 11(8):931. https://doi.org/10.3390/rs11080931
Chicago/Turabian StyleTien Bui, Dieu, Himan Shahabi, Ebrahim Omidvar, Ataollah Shirzadi, Marten Geertsema, John J. Clague, Khabat Khosravi, Biswajeet Pradhan, Binh Thai Pham, Kamran Chapi, and et al. 2019. "Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm" Remote Sensing 11, no. 8: 931. https://doi.org/10.3390/rs11080931