Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance
Abstract
:1. Introduction
2. Overview of the Study Area
3. Methodology
3.1. Data Collection
3.1.1. Landslides Database
3.1.2. Landslide Influencing Attributes (LIAs)
3.2. Research Methods
3.2.1. DIANA Algorithm
3.2.2. ROCK Algorithm
- Load the dataset containing objects
- Select a random sample of objects from the dataset
- Compute the link value for each pair of objects—that is, the number of shared neighbors between objects
- Perform a bottom-up hierarchical clustering on the data based on the link’s similarity measure
- Compute and employ a goodness measure (Equation (2)) to identify the pair of objects to be merged at each step.
- 6.
- Repeat the same procedures and assign the rest of the objects to the clusters that have been created.
- The pair of clusters with maximum is considered to be the best pair to be merged.
3.2.3. Implementation of DIANA and ROCK Clustering Methods in LSM
3.3. Methods for Landslide Susceptibility Classification
3.3.1. K-Means Algorithm
- Define the value of “k”.
- Randomly select k-initial centroids.
- Assign each point to its nearest centroid to form a group.
- Calculate and update the centroid (mean) of each group.
- Repeat steps 3–4 until no point changes the group.
3.3.2. Landslide Density
3.4. Performance Evaluation and Comparison Methods
3.4.1. Performance Evaluation
3.4.2. Comparison Methods
4. Results
4.1. Clustering Analysis
4.2. Landslide Susceptibility Mapping
4.3. Evaluation and Comparison Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Attribute Name | Data Type | Scale | Class | Data Source |
---|---|---|---|---|---|
Topography | Elevation (m) | Continuous | 1:50,000 | 0~254 | Xi’an Center of Geological Survey |
Slope angle (°) | Continuous | 0–6.54, 6.54–13.08, 13.08–18.42, 18.42–22.78, 22.78–26.66, 26.66–30.54, 30.54–34.41, 34.41–39.02, 39.02–61.80 | |||
Slope aspect | Discrete | Flat, North (N), North-East (NE), North-West (NW), East (E), West (W), South-East (SE), South (S), and South-West (SW) | |||
Profile curvature | Discrete | <−0.05, −0.05 to 0.05, >0.05 | |||
Geology | Lithology | Discrete | I: loess + nearly horizontal paleo-soil, II: loess + inclined paleo-soil, III: loess + paleo-soil layers + bedrock, IV: loess + paleo-soil layers + the Neogene clay | Xi’an Center of Geological Survey | |
Underlying surface | NDVI | Continuous | −0.54~0.99 | Xi’an Center of Geological Survey | |
Triggering attribute | Rainfall (mm) | Uncertain | 0~200 | Baota Weather Bureau |
(A) | |||||||||||
Subset Id. | Attribute Values (Before Normalization) | Landslide Density | Susceptibility Level | ||||||||
Elevation | Slope Angle | Profile Curvature | Slope Aspect | Lithology | NDVI | Rainfall | Area (km2) | Land-slides | LD (/km2) | ||
1 | 32.41 | 26.89 | 0.026 | S | II | 0.67 | 32–286 | 9.54 | 8 | 0.84 | HSL |
2 | 25.35 | 21.67 | 0.041 | SE | IV | 0.56 | 24–237 | 8.92 | 0 | 0 | Based on expertise |
… | … | … | … | … | … | … | … | … | … | … | … |
190 | 21.88 | 30.38 | 0.61 | S | III | 0.69 | 38–189 | 12.34 | 9 | 0.73 | MSL |
… | … | … | … | … | … | … | … | … | … | … | … |
(B) | |||||||||||
Subset Id. | Attribute Values (Before Normalization) | Landslide Density | Susceptibility Level | ||||||||
Elevation | Slope Angle | Profile Curvature | Slope Aspect | Lithology | NDVI | Rainfall | Area (km2) | Landslides | LD (/km2) | ||
1 | 29.89 | 24.82 | 0.032 | S | II | 0.77 | 30–283 | 9.53 | 7 | 0.73 | MSL |
2 | 21.99 | 19.19 | 0.043 | N | III | 0.64 | 26–232 | 6.67 | 5 | 0.74 | MSL |
… | … | … | … | … | … | … | … | … | … | … | … |
235 | 14.89 | 39.43 | 0.61 | NE | II | 0.71 | 20–150 | 15.32 | 0 | 0 | Based on expertise |
… | … | … | … | … | … | … | … | … | … | … | … |
Models | DIANA | ROCK |
---|---|---|
tp | 258 | 280 |
tn | 186 | 192 |
fp | 27 | 21 |
fn | 35 | 33 |
St | 0.8805 | 0.8874 |
sp | 0.8732 | 0.9014 |
Kappa | 0.7518 | 0.7828 |
Silhouette | 0.8543 | 0.8677 |
Accuracy | 0.8775 | 0.8933 |
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Mwakapesa, D.S.; Mao, Y.; Lan, X.; Nanehkaran, Y.A. Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance. Sustainability 2023, 15, 4218. https://doi.org/10.3390/su15054218
Mwakapesa DS, Mao Y, Lan X, Nanehkaran YA. Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance. Sustainability. 2023; 15(5):4218. https://doi.org/10.3390/su15054218
Chicago/Turabian StyleMwakapesa, Deborah Simon, Yimin Mao, Xiaoji Lan, and Yaser Ahangari Nanehkaran. 2023. "Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance" Sustainability 15, no. 5: 4218. https://doi.org/10.3390/su15054218
APA StyleMwakapesa, D. S., Mao, Y., Lan, X., & Nanehkaran, Y. A. (2023). Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance. Sustainability, 15(5), 4218. https://doi.org/10.3390/su15054218