Landslide Susceptibility Mapping with Deep Learning Algorithms
<p>Location of the study area.</p> "> Figure 2
<p>Landslides in Maoxian: (<b>a</b>) antecedent landslides (source: Google Earth); (<b>b</b>) some landslides in the study area (source: field visit); (<b>c</b>) geometric structure of the Xinmo landslide [The background image was obtained from <a href="http://www.scgis.net/mxxy/" target="_blank">http://www.scgis.net/mxxy/</a> (accessed on 12 August 2020), and detail of the image is from [<a href="#B36-sustainability-14-01734" class="html-bibr">36</a>]]; and (<b>d</b>) image of the Xinmo landslide. Photographs were taken during the field visit on 2 June 2020.</p> "> Figure 3
<p>Landslide predisposing factors used in this study area: (<b>a</b>) slope angle [°], (<b>b</b>) elevation [m], (<b>c</b>) aspect, (<b>d</b>) plan curvature, (<b>e</b>) SPI, (<b>f</b>) TWI, (<b>g</b>) distance to roads [m], (<b>h</b>) distance to rivers [m], (<b>i</b>) distance to faults [m], (<b>j</b>) NDVI, (<b>k</b>) average annual rainfall [mm/year], (<b>l</b>) geology, and (<b>m</b>) land cover (LC).</p> "> Figure 3 Cont.
<p>Landslide predisposing factors used in this study area: (<b>a</b>) slope angle [°], (<b>b</b>) elevation [m], (<b>c</b>) aspect, (<b>d</b>) plan curvature, (<b>e</b>) SPI, (<b>f</b>) TWI, (<b>g</b>) distance to roads [m], (<b>h</b>) distance to rivers [m], (<b>i</b>) distance to faults [m], (<b>j</b>) NDVI, (<b>k</b>) average annual rainfall [mm/year], (<b>l</b>) geology, and (<b>m</b>) land cover (LC).</p> "> Figure 4
<p>Multicollinearity indices for landslide predisposing factors.</p> "> Figure 5
<p>Feature selection results for landslide predisposing factors. (Slp: slope angle, Ele: elevation, Asp: aspect, Pln: plan curvature, SPI: stream power index, TWI: topographic wetness index, Ro: distance to roads, Ri: distance to rivers, Falt: distance to faults, NDVI: normalized difference vegetation index, Rain: rainfall, Geol: geology, and Land: land cover).</p> "> Figure 6
<p>Landslide susceptibility maps of the study area produced via (<b>a</b>) CNN, (<b>b</b>) DNN, (<b>c</b>) LSTM, and (<b>d</b>) RNN.</p> "> Figure 7
<p>Model-wise landslide percentage in the study area.</p> "> Figure 8
<p>AUC for LSMs of the FR and LR models: (<b>a</b>) success rate curves and (<b>b</b>) prediction rate curves.</p> "> Figure 9
<p>Comparison of DNN-based landslide susceptibility map with a field visit.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Study Area
2.2. Data Acquisition
2.2.1. Landslide Inventory Map
2.2.2. Landslide Predisposing Factors
2.3. Multicollinearity Analysis
2.4. Feature Selection
2.5. Correlation between Landslides Occurrence and Predisposing Factors
2.6. Spatial Modeling
2.6.1. Convolutional Neural Network (CNN)
2.6.2. Deep Neural Network (DNN)
2.6.3. Long Short-Term Memory Networks (LSTM)
2.6.4. Recurrent Neural Network (RNN)
2.7. Model Validation
3. Results and Discussion
3.1. Multicollinearity Assessment
3.2. Pearson Correlation Assessment
3.3. Correlation between Landslide Occurrence and Predisposing Factors
3.4. Landslide Susceptibility Map
3.5. Model Validation of the Landslide Susceptibility Maps
3.6. Validation of the Landslide Susceptibility Map
3.7. Identifying Most Important Factors
3.8. Trigger Mechanisms of Landslides in Moaxian County
4. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Eon | Era | Geological Unit | Lithological Description |
---|---|---|---|
Phanerozoic | Mesozoic | Cretaceous (K) | Clastic rock, argillaceous rock |
Triassic (T) | Gray limestone, gray and sandy mudstones, thick feldspathic quartzose sandstone interlayered with shale | ||
Paleozoic | Permian (P) | Thick limestone intercalated with slate, carbonate-rich shale, and claystone | |
Carboniferous (C) | Epimetamorphic carbonate rock intercalated with clastic rock | ||
Ordovician (O) | Thin–medium-thick argillaceous limestone | ||
Cambrian (ϵ) | Black siliceous phyllite, black silicalite intercalated with siliceous slate | ||
Precambrian | Proterozoic | Neoproterozoic (Z) | Volcanic and pyroclastic rock formation, tilly clastic rock formation |
Mesoproterozoic (Y) | Clastic rock formation and carbonate rock formation |
Factor | Classes | Source of Data | Reference |
---|---|---|---|
Slope angle (degrees) | (i) 0–15, (ii) 15–30, (iii) 30–45, (iv) 45–60, and (v) >60 Method: manual classification | Extracted from DEM: 30 m × 30 m | Moharrami et al. [17], Tang et al. [43] |
Elevation (m) | (i) <1750, (ii) 1750–2600, (iii) 2600–3450, (iv) 3450–4300, and (v) >4300 Method: manual classification | https://earthexplorer.usgs.gov/ (accessed on 12 August 2020) DEM: 30 m × 30 m | Intarawichian and Dasananda [44] |
Aspect | (i) Flat, (ii) North, (iii) Northeast, (iv) East, (v) Southeast, (vi) South, (vii) Southwest, (viii)West, and (ix) Northwest | Extracted from DEM: 30 m × 30 m | Vijith and Madhu [45], Kayastha [46] |
Plan curvature | (i) Concave, (ii) Flat, and (iii) Convex Method: manual classification | Extracted from DEM: 30 m × 30 m | Nefeslioglu et al. [47], Yilmaz et al. [48] |
SPI | (i) 0–5, (ii) 5–10, (iii) 10–15, (iv) 15–20, and (v) >20 Method: manual classification | Equation (1) Source from DEM: 30 m × 30 m | Mind’je et al. [42] |
TWI | (i) <4, (ii) 4–6, (iii) 6–8, (iv) 8–10, and (v) >10 Method: manual classification | Equation (2) Source from DEM: 30 m × 30 m | Sørensen et al. [49] |
Distance to roads (m) | (i) 0–1000, (ii) 1000–2000, (iii) 2000–3000, (iv) 3000–4000, (v) 4000–6000, (vi) 6000–8000, and (vii) >8000 Method: manual classification | Extracted from Google earth | Acharya and Lee [50] |
Distance to rivers (m) | (i) 0–1000, (ii) 1000–2000, (iii) 2000–3000, (iv) 3000–4000, (v) 4000–6000, (vi) 6000–8000, and (vii) >8000 Method: manual classification | Extracted from Google earth | Myronidis et al. [51] |
Distance to faults (m) | (i) 0–2000, (ii) 2000–4000, (iii) 4000–6000, (iv) 6000–8000, and (v) >8000 Method: manual classification | Digitizing a geological map: Scale-1:100,000 | Fan et al. [10], Meng et al. [52] |
Normalized Difference Vegetation Index (NDVI) | (i) −0.20–0.27, (ii) 0.27–0.44, (iii) 0.44–0.59, (iv) 0.59–0.70, and (v) 0.70–0.91 Method: manual classification | https://ladsweb.modaps.eosdis.nasa.gov/ (accessed on 12 August 2020) | Mind’je et al. [42] |
Annual mean rainfall (mm/y) | (i) <700, (ii) 700–750, (iii) 750–800, (iv) 800–850, and (v) >850 Method: manual classification | chg.geog.ucsb.edu/data/chirps: (accessed on 12 August 2020) | Nahayo et al. [4], Mind’je et al. [42] |
Geology | (i) Triassic, (ii) Mesoproterozoic, (iii) Permian, (iv) Cretaceous, (v) Carboniferous, (vi) Ordovician, (vii) Cambrian, and (viii) Neoproterozoic Method: manual classification | Digitizing a geological map: Scale-1:100,000 | Meng et al. [52] |
Land Cover (LC) | (i) Built area, (ii) Cropland, (iii) Grassland, (iv) Shrubland, (v) Snow and ice, (vi) Tree cover, and (vii) Water bodies Method: manual classification | https://earthexplorer.usgs.gov/ (accessed on 12 August 2020) | Vijith and Madhu [45], Mind’je et al. [42] |
Factor | Factor Class | No. of Pixels in Domain | % of Domain | No. of Landslides | % of Landslides | FR | RF | PR |
---|---|---|---|---|---|---|---|---|
Slope (degrees) | 0–15 15–30 30–45 45–60 >60 | 327,792 1,494,388 1,876,163 491,562 24,476 | 7.78 35.46 44.52 11.66 0.58 | 307 2344 6644 3366 270 | 2.37 18.13 51.38 26.03 2.09 | 0.31 0.51 1.15 2.23 3.60 | 0.04 0.07 0.15 0.29 0.46 | 8.45 |
Elevation (m) | <1750 1750–2600 2600–3450 3450–4300 >4300 | 261,623 1,039,419 1,566,899 1,194,877 151,563 | 6.21 24.66 37.18 28.35 3.60 | 2461 6128 4096 246 0 | 19.00 47.40 31.70 1.90 0.00 | 3.07 1.92 0.85 0.07 0.00 | 0.52 0.33 0.14 0.01 0.00 | 10.40 |
Aspect | Flat North Northeast East Southeast South Southwest West Northwest | 2586 489,205 545,870 570,251 554,519 485,600 491,043 526,368 548,572 | 0.06 11.61 12.95 13.53 13.16 11.52 11.65 12.49 13.02 | 9 834 1056 1456 2412 2229 2260 1805 870 | 0.10 6.40 8.20 11.30 18.70 17.20 17.50 14.00 6.70 | 1.13 0.56 0.63 0.83 1.42 1.50 1.50 1.12 0.50 | 0.12 0.06 0.07 0.09 0.15 0.16 0.16 0.12 0.06 | 2.14 |
Plan curvature | <−1(concave) 0 >1(Convex) | 649,075 1,474,554 2,090,752 | 15.40 34.99 49.61 | 2174 4251 6506 | 16.80 32.90 50.30 | 1.09 0.94 1.01 | 0.36 0.31 0.33 | 1.00 |
SPI | 0–5 5–10 10–15 15–20 >20 | 1,159,179 44,171 113,106 165,664 2,732,261 | 27.51 1.05 2.68 3.93 64.83 | 3061 51 134 302 9383 | 23.70 0.40 1.00 2.30 72.60 | 0.86 0.38 0.39 0.59 1.12 | 0.26 0.11 0.12 0.18 0.34 | 4.46 |
TWI | <4 4–6 6–8 8–10 >10 | 657,487 2,290,162 913,555 253,173 100,004 | 15.60 54.34 21.68 6.01 2.37 | 2873 6515 2790 623 130 | 22.20 50.40 21.60 4.80 1.00 | 1.42 0.93 1.00 0.80 0.42 | 0.31 0.20 0.22 0.18 0.09 | 4.38 |
Distance to roads (m) | 0−1000 1000−2000 2000–3000 3000–4000 4000–6000 6000–8000 >8000 | 352,201 320,976 311,409 301,292 559,651 4,558,441 1,913,007 | 8.36 7.62 7.39 7.15 13.28 10.82 45.39 | 4238 2698 1310 979 1973 835 898 | 32.80 20.90 10.10 7.60 15.30 6.50 6.90 | 3.92 2.74 1.37 1.06 1.15 0.60 0.15 | 0.38 0.27 0.13 0.10 0.11 0.06 0.01 | 5.60 |
Distance to faults (m) | 0−20002000−4000 4000−6000 6000−8000 > 8000 | 859,855 660,187 516,426 383,974 1,793,939 | 20.40 15.67 12.25 9.11 42.57 | 9337 2282 570 252 490 | 72.20 17.60 4.40 1.90 3.80 | 3.54 1.13 0.36 0.21 0.09 | 0.66 0.21 0.07 0.04 0.02 | 12.97 |
NDVI | −0.20–0.27 0.27–0.44 0.44–0.59 0.59–0.70 0.70–0.91 | 115,241 275,557 822,723 1,385,952 1,614,908 | 2.73 6.54 19.52 32.89 38.32 | 138 574 1612 4376 6231 | 1.07 4.44 12.47 33.84 48.19 | 0.39 0.68 0.64 1.03 1.26 | 0.10 0.17 0.16 0.26 0.31 | 4.34 |
Rainfall | <700 700−750 750−800 800−850 >850 | 178,320 668,284 1,096,458 1,905,695 365,624 | 4.23 15.86 26.02 45.22 8.68 | 252 5935 1457 4659 628 | 1.95 45.90 11.27 36.03 4.86 | 0.46 2.89 0.43 0.80 0.56 | 0.09 0.56 0.08 0.15 0.11 | 9.58 |
Geology | Triassic Mesoproterozoic Permian Cretaceous Carboniferous Ordovician Cambrian Neoproterozoic | 1,875,665 41,842 100,055 606,215 1,206,402 176,153 144,813 63,705 | 44.50 0.99 2.37 14.38 28.62 4.18 3.44 1.51 | 6428 0 300 923 4057 391 801 31 | 49.71 0.00 2.32 7.14 31.37 3.02 6.19 0.24 | 1.12 0.00 0.98 0.50 1.10 0.72 1.80 0.16 | 0.18 0.00 0.15 0.08 0.17 0.11 0.28 0.02 | 5.67 |
Land cover | Cropland Shrub land Tree cover Grassland Built-up area Waterbodies Snow and ice cover | 186,651 229,970 3,318,735 471,725 3181 3558 547 | 4.43 5.46 78.75 11.19 0.08 0.08 0.01 | 1460 761 9601 1105 3 1 0 | 11.29 5.89 74.25 8.55 0.02 0.01 0.00 | 2.55 1.08 0.94 0.76 0.31 0.09 0.00 | 0.45 0.19 0.16 0.13 0.05 0.02 0.00 | 8.91 |
Algorithm | Training Dataset | Testing Dataset | ||||||
---|---|---|---|---|---|---|---|---|
Recall | F1 Score | AUC | Log-Loss | Recall | F1 Score | AUC | Log-Loss | |
CNN | 0.790 | 0.788 | 0.866 | 0.449 | 0.803 | 0.789 | 0.856 | 0.496 |
DNN | 0.943 | 0.828 | 0.877 | 0.440 | 0.838 | 0.805 | 0.873 | 0.438 |
LSTM | 0.856 | 0.826 | 0.883 | 0.423 | 0.888 | 0.801 | 0.865 | 0.491 |
RNN | 0.921 | 0.804 | 0.845 | 0.479 | 0.833 | 0.777 | 0.829 | 0.848 |
Factor | Regression Coefficient, β | Standard Error | t | Pr > |t| | Lower Bound (95%) | Upper Bound (95%) | Rank |
---|---|---|---|---|---|---|---|
Slope angle | 0.252 | 0.017 | 14.902 | <0.0001 | 0.218 | 0.285 | 1 |
Rainfall | 0.110 | 0.019 | 5.859 | <0.0001 | 0.073 | 0.147 | 2 |
Distance to faults | 0.102 | 0.020 | 4.995 | <0.0001 | 0.062 | 0.141 | 3 |
Plan curvature | 0.034 | 0.018 | 1.924 | 0.054 | −0.001 | 0.069 | 4 |
Aspect | −0.027 | 0.016 | −1.726 | 0.085 | −0.058 | 0.004 | 5 |
Distance to roads | −0.079 | 0.023 | −3.500 | 0.000 | −0.124 | −0.035 | 6 |
Geology | −0.110 | 0.021 | −5.345 | <0.0001 | −0.150 | −0.070 | 7 |
SPI | −0.112 | 0.020 | −5.576 | <0.0001 | −0.151 | −0.072 | 8 |
Land cover | −0.134 | 0.017 | −8.104 | <0.0001 | −0.167 | −0.102 | 9 |
Elevation | −0.521 | 0.024 | −22.060 | <0.0001 | −0.567 | −0.475 | 1 |
TWI | 0.000 | 0.000 | - | - | - | - | 11 |
NDVI | 0.000 | 0.000 | - | - | - | - | 12 |
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Habumugisha, J.M.; Chen, N.; Rahman, M.; Islam, M.M.; Ahmad, H.; Elbeltagi, A.; Sharma, G.; Liza, S.N.; Dewan, A. Landslide Susceptibility Mapping with Deep Learning Algorithms. Sustainability 2022, 14, 1734. https://doi.org/10.3390/su14031734
Habumugisha JM, Chen N, Rahman M, Islam MM, Ahmad H, Elbeltagi A, Sharma G, Liza SN, Dewan A. Landslide Susceptibility Mapping with Deep Learning Algorithms. Sustainability. 2022; 14(3):1734. https://doi.org/10.3390/su14031734
Chicago/Turabian StyleHabumugisha, Jules Maurice, Ningsheng Chen, Mahfuzur Rahman, Md Monirul Islam, Hilal Ahmad, Ahmed Elbeltagi, Gitika Sharma, Sharmina Naznin Liza, and Ashraf Dewan. 2022. "Landslide Susceptibility Mapping with Deep Learning Algorithms" Sustainability 14, no. 3: 1734. https://doi.org/10.3390/su14031734
APA StyleHabumugisha, J. M., Chen, N., Rahman, M., Islam, M. M., Ahmad, H., Elbeltagi, A., Sharma, G., Liza, S. N., & Dewan, A. (2022). Landslide Susceptibility Mapping with Deep Learning Algorithms. Sustainability, 14(3), 1734. https://doi.org/10.3390/su14031734