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

Puente-Sotomayor, Fernando; Mustafa, Ahmed; Teller, Jacques

doi:10.1186/s40677-021-00184-0

Geoenvironmental Disasters

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
Reference code	Approach, Models, Techniques	Cell Size for resolution (m)	Year	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 Accumulation^c	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		✓	✓						✓				✓		✓				✓		✓					✓
3^a	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	✓	✓	✓	✓	✓		✓	✓
10^a	WLC	9	2018		✓	✓					✓					✓		✓							✓	✓						✓	✓
11	IV, LR	30	2017	✓	✓	✓										✓		✓				✓		✓					✓
12	AHP, WLC, MCE	10	2015		✓	✓						✓				✓		✓	✓	✓		✓		✓					✓
13	AHP, WLC, OWA	20	2014	✓	✓	✓						✓				✓		✓				✓		✓					✓
14^a	MCE, AHP, WLC	1, 10, 50	2014	✓	✓	✓																✓		✓			✓	✓
15^b	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		✓					✓	✓		✓			✓	✓	✓	✓			✓			✓				✓	✓
19^a	MCE, WLC	5	2010		✓											✓		✓
20^a	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

^aCorrespond to LSM studies in urban areas
^bThis modelling presented 35 factors; a summarized version of 13 is presented in this table
^cFor this classification, these factors have been grouped in the hydrology. However, they are also considered in the topography
^dOther DTM derivatives include: openness, side exposure index, hillshade, flow direction, and roughness
Notes:
1. Diversity of factors for modelling have been classified by authors, criteria may vary
2. The selection of factors varies according to approaches. For instance, 7, 8, and 9 involve a wide range of topography-related factors
References:
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)
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

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