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Table 1 Review on landslide susceptibility modelling (LSM) modelling techniques, factors and inputs resolution used

From: Landslide Susceptibility Mapping of Urban Areas: Logistic Regression and Sensitivity Analysis applied to Quito, Ecuador

Reference code Approach, Models, Techniques Cell Size for resolution (m) Year Topography / DTM derivatives Rainfall / Climate Geology / Land Cover Roads Hydrology Seismicity Anthropic, other
Elevation / height /scarp Slope angle / gradient Slope aspect / sunlight exposition / orientation Curvatures Topographic Position Index (TPI) Topographic Roughness Index (TRI) Slope / Flow Direction Other DTM derivatives d Annual Precipitation Intense Precipitations Long Precipitations Potential rain effect (PRE) Lithology / Geology Geo (morpho) logical Units Land Use / Vegetation Cover Soils / textures Normalized difference vegetation index (NDVI) Potential erosion index (PEI) Distance to roads Road Density Distance to drainage / streams River / Gully Density Flow Accumulationc Topographic Wetness Index (TWI)c Stream Power Index (SPI) Distance to faults Ground Peak Acceleration Seismic Intensity / other Tectonic Features Artificial Intervention / structures density Declared flows Random Value / other
1 FR, MCE + AHP, OB 10 2020                           
2 AHP, WLC - LSI 10 2020                               
3a DL 10 2020                          
4 FR, IV, CF, LR 30 2020                           
5 LR, RF, SVM 5–30 2019                         
6 LR, RF 4–50 2019                           
7 AHP, FR, MCE 30 2019                             
8 JT, SI 15 2019                              
9 SA – PBA, ML, FFNN, SVM 1–30 2018                          
10a WLC 9 2018                             
11 IV, LR 30 2017                             
12 AHP, WLC, MCE 10 2015                             
13 AHP, WLC, OWA 20 2014                             
14a MCE, AHP, WLC 1, 10, 50 2014                            
15b SA – MURs, LCVs, RF 10–500 2013                          
16 ANN 20 2013                            
17 RF, SA 10, 20, 50, 100, 250, 500 2013                           
18 SA - ANN 50 2011                           
19a MCE, WLC 5 2010                                
20a WeF, MuF 20 2009                                
Total     12 20 16 9 3 2 3 6 8 2 0 1 14 2 14 4 2 1 10 2 10 5 3 5 2 9 1 1 2 1 1
  1. aCorrespond to LSM studies in urban areas
  2. bThis modelling presented 35 factors; a summarized version of 13 is presented in this table
  3. cFor this classification, these factors have been grouped in the hydrology. However, they are also considered in the topography
  4. dOther DTM derivatives include: openness, side exposure index, hillshade, flow direction, and roughness
  5. Notes:
  6. 1. Diversity of factors for modelling have been classified by authors, criteria may vary
  7. 2. The selection of factors varies according to approaches. For instance, 7, 8, and 9 involve a wide range of topography-related factors
  8. References:
  9. 1. Gudiyangada Nachappa et al. (2020), 2. Psomiadis et al. (2020), 3. Lee, Baek, Jung, & Lee et al. (2020), 4. Wubalem (2020), 5. Chang et al. (2019), 6. Sîrbu et al. (2019), 7. Meena et al. (2019), 8. Ramos-Bernal et al. (2019), 9. Pawluszek et al. (2018), 10. Lara et al. (2018), 11. Du et al. (2017), 12. Shahabi and Hashim (2015), 13. Feizizadeh and Blaschke (2014), 14. Dragićević et al. (2014), 15. Catani et al. (2013b), 16. Pascale et al. (2013), 17. Catani et al. (2013a), 18. Melchiorre et al. (2011)), 19. Klimeš and Rios Escobar (2010), 20. Bathrellos et al. (2009)
  10. Abbreviations: AHP analytical hierarchical process, ANN artificial neural networks, CF certainty factor, DL deep learning, DTM digital terrain model, FFNN feed-forward neural network, FR data-driven frequency ratio, IV information value, JT jackknife test, LCVs landslide conditioning factors, LR logistic regression, LSI landslide susceptibility index, MCE multi-criteria evaluation, ML maximum likelihood, MuF multiple factor model, MURs mapping unit resolutions, NDVI normalized difference vegetation index, OB object based approaches, OWA ordered weighted average, PBA pixel based approaches, RF random forest, SA sensitivity analysis, SI susceptibility index, SVM support vector machine, WeF weight factor model, WLC weighted linear combination