Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador
Abstract
:1. Introduction
2. Materials
Study Area
3. Methods
3.1. Obtaining the Landslide Inventory
3.2. Generation of Conditioning Factors
3.3. Extraction of Training and Test Datasets
3.4. Implementation of the ANN-MLP Algorithm
Hyperparameter Settings
3.5. Results Validation
3.6. Obtaining Landslide Susceptibility Maps (LSMs)
4. Results
4.1. Correlation Analysis between Conditioning Factors
4.2. Landslide Analysis According to Conditioning Factors
4.3. ANN Implementation and Performance
4.4. Results Validation
4.5. Landslide Susceptibility Analysis
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Information | Element/Process Obtained | Source | Scale/Resolution |
---|---|---|---|
Satellite images and orthophotos for landslide inventory | Photo interpretation | Planet | 5 m |
Ortophoto | 30 cm | ||
DInSAR | Copernicus-Sentinel 1 | 5 × 20 m | |
COSMO-SkyMed | 1 m | ||
Landslide inventory | Rotational landslides | IERSE | - |
Digital elevation model (DEM): Topographical maps | Aspect, curvature, elevation, slope, SPI, TWI | SIGTIERRAS-IERSE | 3 m |
Geological map | Lithology | SNI | 1:100,000 |
Soil cover: Land cover map Road layer | Land cover | SIGTIERRAS | 1:25,000 |
Distance to roads | IGM | ||
Hydrological: River layer | Distance to rivers | IGM | 1:25,000 |
Global Factor | Conditioning Factor | Data Range (Unit) |
---|---|---|
Topographical | Elevation | (i) <2583, (ii) 2583–2855, (iii) 2855–3127, (iv) 3127–3398, (v) >3398 (m). |
Slope | (i) <16, (ii) 16–32, (iii) 32–48, (iv) 48–64, (v) >64 (degree). | |
Aspect | Flat zones −1°. 0°–22.5°(N); 22.5°–67.5° (NE); 67.5°–112.5° (E); 112.5°–157.5° (SE); 157.5°–202.5° (S); 202.5°–247.5° (SW); 247.5°–292.5° (W); 292.5°–337.5° (NW); 337.5°–360° (N). | |
SPI | (i) <−2.8, (ii) −2.8–−1, (iii) −1–0.8, (iv) 0.8–2.6, (v) >2.6. | |
TWI | (i) <3.9, (ii) 3.9–5.7, (iii) 5.7–7.5, (iv) 7.5–9.3, (v) >9.3. | |
Curvature | (i) < −4.7, (ii) −4.7–−1.8, (iii) −1.8–1.1, (iv) 1.1–4 (v) >4. | |
Geological | Lithology | Principally 17: sandy clays; light laminated shales with gypsum; silts, clays, sands, gravels, and blocks; heterogeneous mixture of fine materials and fine angular fragments without stratification; heterogeneous mixture of fine materials and fine angular fragments (various sizes); medium to coarse-grained tobaceous sandstones; siltstones, shales and fine-grained sandstones; sandy silt, silty clay; coarse andesitic conglomerates; silts and clays; red clays with sandstones and conglomerates alternation; volcanic agglomerate; sands, silts, clays, and conglomerates; laminated mudstones, dark tobaceous sandstones; variable proportion of silts, clays, sands, gravels, and blocks; tuffs and agglomerates; massive siltstones. |
Soil covers | Land cover | A total of 1903 coverages classified in 11 classes: forest plantation, grassland, populated area, shrub vegetation, herbaceous vegetation, crop, anthropic infrastructure, wasteland, water body, moorland, and native forest. |
Distance to roads | (i) <452, (ii) 452–905, (iii) 905–1357, (iv) 1357–1810, (v) >1810 (m). | |
Hydrological | Distance to rivers | (i) <328, (ii) 328–656, (iii) 656–984, (iv) 984–1312, (v) >1.312 (m). |
Global Factor | Conditioning Factor | Relevance |
---|---|---|
Topographical | Elevation | The probability of landslide occurrence is larger in areas where the elevation is higher [71]. |
Slope | Increasing slope decreases stability [55]. Its angle is considered as a controlling factor in landslide modeling [72]. | |
Aspect | Indicates exposure to local climatic conditions [55] and atmospheric processes such as rainfall, wind, and solar radiation [73]. | |
Curvature | Refers to a change in the slope gradient or aspect in a given direction [6]. Affects the control of water flow [33]. | |
Stream power index (SPI) | Indicator of erosive processes caused by surface runoff. High SPI values indicate proximity to a stream. With low SPI values, a low susceptibility to landslide initiation is expected [55]. | |
Topographic wetness index (TWI) | Indicator of saturated soil conditions during rainfall and sediment accumulation [55]. With a higher value of TWI, there is a greater tendency for the saturation of slope materials [73]. | |
Geological | Lithology | Defines material where landslides occur [31] and influences geomechanical characteristics of terrain [72]. |
Soil covers | Land cover | Each cover has characteristics and textures that influence landslide generation. It is also related to the degree of vegetation cover that influences the stability of slope materials [33]. |
Distance to roads | Related to the process of road construction, which, when developed in mountainous areas, causes impacts on slope stability [25]. | |
Hydrological | Distance to rivers | The closer this distance, the greater the probability of landslides [74]. It should be considered that a stream may be where landslides move to, which generates additional risk [75]. |
Hyperparameter | Description | Setting Value |
---|---|---|
act.fct | Differentiable activation function [79] (no configuration was required in this research). | logistic (per default) |
algorithm | Define algorithm to be implemented. | RPROP+, RPROP−, SLR, SAG, backprop |
err.fct | Define the error function [92]. Used for the error calculation. | ce (cross entropy) |
hidden | Define the number of hidden layers and neurons [79]. | 3 (one hidden layer with three neurons) |
linear.output | If act.fct is not set, its default value is TRUE [79]. Change to FALSE for classification models. | FALSE |
learningrate | Learning rate [79]. Applies only when algorithm = backprop. | 0.01 |
stepmax | Define the maximum number of steps for ANN training [90]. | 1e + 8 |
Aspect | Curvature | Elevation | Dist. Rivers | Dist. Roads | Land Cover | Lithology | Slope | SPI | TWI | |
---|---|---|---|---|---|---|---|---|---|---|
Aspect | 1 | |||||||||
Curvature | 0.00 * | 1 | ||||||||
Elevation | 0.02 | 0.03 | 1 | |||||||
Dist. rivers | −0.02 | 0.04 | 0.17 | 1 | ||||||
Dist. roads | 0.11 | 0.01 | 0.28 | −0.21 | 1 | |||||
Land cover | −0.10 | −0.01 | −0.06 | 0.18 | −0.37 | 1 | ||||
Lithology | −0.05 | 0.02 | 0.29 | 0.05 | 0.09 | 0.06 | 1 | |||
Slope | 0.11 | 0.02 | 0.25 | −0.17 | 0.43 | −0.41 | 0.05 | 1 | ||
SPI | 0.07 | −0.33 | 0.15 | −0.09 | 0.25 | −0.22 | 0.01 | 0.52 | 1 | |
TWI | −0.09 | −0.34 | −0.15 | 0.10 | −0.22 | 0.24 | −0.02 | −0.56 | 0.33 | 1 |
Algorithm | Runtime (Seconds) | Runtime (Minutes) |
---|---|---|
RPROP+ | 7.1 | ~0.12 |
RPROP− | 6.9 | ~0.12 |
SLR | 70.7 | ~1.18 |
SAG | 9.8 | ~0.16 |
Backprop | 560.7 | ~9.35 |
Algorithm | AUC (Training) | AUC (Testing) |
---|---|---|
RPROP+ | 0.881 | 0.714 |
RPROP− | 0.888 | 0.761 |
SLR | 0.870 | 0.712 |
SAG | 0.889 | 0.711 |
Backprop | 0.867 | 0.707 |
Algorithm | Training | |||
---|---|---|---|---|
Sens | Accuracy | PPV | F-Score | |
RPROP+ | 0.887 | 0.837 | 0.900 | 0.894 |
RPROP− | 0.873 | 0.850 | 0.940 | 0.905 |
SLR | 0.812 | 0.820 | 0.996 | 0.894 |
SAG | 0.853 | 0.844 | 0.961 | 0.904 |
Backprop | 0.946 | 0.837 | 0.834 | 0.887 |
Algorithm | Testing | |||
---|---|---|---|---|
Sens | Accuracy | PPV | F-Score | |
RPROP+ | 0.832 | 0.748 | 0.839 | 0.836 |
RPROP− | 0.815 | 0.764 | 0.894 | 0.853 |
SLR | 0.789 | 0.775 | 0.964 | 0.868 |
SAG | 0.800 | 0.754 | 0.905 | 0.849 |
Backprop | 0.846 | 0.721 | 0.776 | 0.809 |
LSM (RPROP+) | |||
---|---|---|---|
Susceptibility | Pixel Amount | Pixels (%) | Landslides (%) |
Very low | 8,406,238 | 20.01 | 11.9 |
Low | 8,294,697 | 19.74 | 14.6 |
Medium | 8,468,934 | 20.16 | 17.4 |
High | 8,396,090 | 19.98 | 28.5 |
Very high | 8,449,935 | 20.11 | 26.8 |
LSM (RPROP−) | |||
Susceptibility | Pixel amount | Pixels (%) | Landslides (%) |
Very low | 8,590,183 | 20.45 | 11.0 |
Low | 8,217,572 | 19.56 | 23.4 |
Medium | 8,374,349 | 19.93 | 19.3 |
High | 8,410,428 | 20.02 | 18.7 |
Very high | 8,423,362 | 20.05 | 26.8 |
LSM (SLR) | |||
Susceptibility | Pixel amount | Pixels (%) | Landslides (%) |
Very low | 10,007,252 | 23.82 | 14.7 |
Low | 1,383,762 | 3.29 | 4.0 |
Medium | 18,820,629 | 44.79 | 45.7 |
High | 3,968,797 | 9.45 | 9.8 |
Very high | 7,835,454 | 18.65 | 25.0 |
LSM (SAG) | |||
Susceptibility | Pixel amount | Pixels (%) | Landslides (%) |
Very low | 8,585,964 | 20.44 | 17.4 |
Low | 8,360,306 | 19.90 | 18.7 |
Medium | 9,881,636 | 23.52 | 15.7 |
High | 6,792,878 | 16.17 | 21.2 |
Very high | 8,395,110 | 19.98 | 26.3 |
LSM (Backprop) | |||
Susceptibility | Pixel amount | Pixels (%) | Landslides (%) |
Very low | 11,818,586 | 28.13 | 23.4 |
Low | 6,182,545 | 14.71 | 11.9 |
Medium | 5,528,573 | 13.16 | 15.9 |
High | 6,894,785 | 16.41 | 12.7 |
Very high | 11,591,405 | 27.59 | 35.3 |
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Bravo-López, E.; Fernández Del Castillo, T.; Sellers, C.; Delgado-García, J. Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador. Remote Sens. 2022, 14, 3495. https://doi.org/10.3390/rs14143495
Bravo-López E, Fernández Del Castillo T, Sellers C, Delgado-García J. Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador. Remote Sensing. 2022; 14(14):3495. https://doi.org/10.3390/rs14143495
Chicago/Turabian StyleBravo-López, Esteban, Tomás Fernández Del Castillo, Chester Sellers, and Jorge Delgado-García. 2022. "Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador" Remote Sensing 14, no. 14: 3495. https://doi.org/10.3390/rs14143495
APA StyleBravo-López, E., Fernández Del Castillo, T., Sellers, C., & Delgado-García, J. (2022). Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador. Remote Sensing, 14(14), 3495. https://doi.org/10.3390/rs14143495