Shallow landslide susceptibility, Rupinaro catchment, Liguria (northwestern Italy)

Figures & data

Figure 1. Relief terrain of the Rupinaro catchment, located in the eastern Liguria region (northwestern Italy): (a) aspect and (b) slope morphometric parameters.

Figure 2. Inventory map of the IFFI (Trigila et al., 2008) and autumn of 2014 rainfall-induced landslides, affecting the Rupinaro catchment. The shaded relief derived from the high-resolution DTM obtained from the post-event LiDAR survey.

Table 1. LiDAR survey parameters.

Parameter	Value
Sensor	RIEGL LMS-Q680i
Vehicle	Eurocopter AS 350
Survey date	Jan 2015
Point density	4 and 9 points/m^2
DTM cellsize	25 cm
Orthoimage ground resolution	15 cm/pixel
Horizontal datum	ETRF2000 UTM zone 32N
Vertical datum	EGM2008

Figure 3. Scheme of the adopted methodology.

Table 2. Scale of preference between two parameters in AHP (Saaty, 1977).

Scales	Degree of preferences	Explanation
1	Equally	Two factors contribute equally to the objective
3	Moderately	Experience and judgment slightly to moderately favor one factor over another
5	Strongly	Experience and judgment strongly or essentially favor one factor over another
7	Very strongly	A factor is strongly favored over another and its dominance is showed in practice
9	Extremely	The evidence of favoring one factor over another is of the highest degree possible of an affirmation
2,4,6,8	Intermediate	Used to represent compromises between the preferences in weights 1, 3, 5, 7 and 9
Reciproclas	Opposites	Used for inverse comparison

Notes: If the factors have direct relationship the scale ranging from 1 to 9, while if there is an in verse relationship the scale vary from 1/2 and 1/9.

Table 3. Pair-wise comparison matrix and normalized principal eigenvector for landslide causative factors and for the classes within each factor, as required for applying the AHP method.

Causative factors and factor classes	Pair-wise comparison matrix					Normalized principal eigenvector
	[1]	[2]	[3]	[4]	[5]
Causative factors
[1] Slope	1	2	5	6		0.52
[2] Land Use		1	4	2		0.26
[3] Lithology			1	1/3		0.07
[4] Aspect				1		0.14
Slope
[1] 0–15${}^{\circ }$	1	1	1/4	1/5	3	0.10
[2] 15–25${}^{\circ }$		1	1/3	1/4	3	0.11
[3] 25–35${}^{\circ }$			1	1/2	5	0.29
[4] 35–45${}^{\circ }$				1	6	0.44
[5] 45–90${}^{\circ }$					1	0.05
Land use
[1] Agricultural areas	1	5	9			0.75
[2] Forest		1	3			0.18
[3] Artificial areas			1			0.07
Lithology
[1] shGOT	1	9	8			0.80
[2] shmGOT		1	1/2			0.08
[3] mlFMA			1			0.12
Aspect
[1] 315–45${}^{\circ }$	1	1/6	1/5			0.08
[2] 45–135/225–315${}^{\circ }$		1	2			0.58
[3] 135–225${}^{\circ }$			1			0.34

Figure 4. Landslide susceptibility zonation map of the Rupinaro catchment; dark gray polygons correspond to the shallow landslides occurred during the autumn 2014 rainfall events. (a) Histogram of percentage values of basin areas and landslide areas in the four susceptibility classes.

Table 4. Consistency index (CI), random consistency index (RI) and consistency ratio (CR) values for the causative factors considered.

Factors	Number	CI	RI	CR
Causative factors	4	0.05	0.9	0.05
Slope	5	0.03	1.12	0.03
Land use	3	0.01	0.58	0.025
Lithology	3	0.02	0.58	0.03
Aspect	3	0.015	0.58	0.025

Figure 5. ROC curve computed on the basis of a comparison between landslide grid cells and landslides susceptibility grid.

Trigila, A. , Iadanza, C. , & Spizzichino, D. (2008). IFFI project (Italian landslide inventory) and risk assessment. Proceedings of the first world landslide forum (pp. 18–21).

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Saaty, T. L. (1977, June). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology , 15 (3), 234–281. 0022249677900335 doi: 10.1016/0022-2496(77)90033-5

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