land

Article
GIS-Based Landslide Susceptibility Mapping for Land Use
Planning and Risk Assessment
Anna Roccati 1 , Guido Paliaga 1, * , Fabio Luino 1 , Francesco Faccini 1,2 and Laura Turconi 1

1 National Research Council, Research Institute for Geo-Hydrological Protection, Strada delle Cacce 73,
10135 Turin, Italy; anna.roccati@irpi.cnr.it (A.R.); fabio.luino@irpi.cnr.it (F.L.); laura.turconi@irpi.cnr.it (L.T.)
2 Department of Earth, Environmental and Life Sciences, University of Genoa, Corso Europa 26, 16132 Genoa,
Italy; faccini@unige.it
* Correspondence: guido.paliaga@irpi.cnr.it

Abstract: Landslide susceptibility mapping is essential for a suitable land use managing and risk
assessment. In this work a GIS-based approach has been proposed to map landslide susceptibility
in the Portofino promontory, a Mediterranean area that is periodically hit by intense rain events
that induce often shallow landslides. Based on over 110 years landslides inventory and experts’
judgements, a semi-quantitative analytical hierarchy process (AHP) method has been applied to
assess the role of nine landslide conditioning factors, which include both natural and anthropogenic
elements. A separated subset of landslide data has been used to validate the map. Our findings
reveal that areas where possible future landslides may occur are larger than those identified in the
actual official map adopted in land use and risk management. The way the new map has been
compiled seems more oriented towards the possible future landslide scenario, rather than weighting
with higher importance the existing landslides as in the current model. The paper provides a useful



decision support tool to implement risk mitigation strategies and to better apply land use planning.

 Allowing to modify factors in order to local features, the proposed methodology may be adopted in
Citation: Roccati, A.; Paliaga, G.; different conditions or geographical context featured by rainfall induced landslide risk.
Luino, F.; Faccini, F.; Turconi, L.
GIS-Based Landslide Susceptibility Keywords: shallow landslides; analytic hierarchy process (AHP); landslide susceptibility mapping;
Mapping for Land Use Planning and land planning; risk assessment; Ligurian coast; Mediterranean area
Risk Assessment. Land 2021, 10, 162.
https://doi.org/10.3390/land10020162

Academic Editor: 1. Introduction

Andrew C Millington
Landslides are among the most hazardous natural instability processes, which globally
Received: 30 December 2020
Accepted: 27 January 2021
lead enormous socio-economical losses and damage to property every year [1–4]. Shallow
Published: 5 February 2021
landslides and debris flows represent one of the major causes of destruction to structures
and infrastructures, injured people and casualties in mountain and hilly regions: they are
Publisher’s Note: MDPI stays neutral
usually triggered by short and severe rainfalls. Shallow landslides generally involve small
with regard to jurisdictional claims in
volumes, unlike debris flows. However, both can be extremely damaging due to their
published maps and institutional affil- widespread spatial distribution across territories, rapid development and high velocity of
iations. propagation [5].
According to Varnes [6], landslide risk can be defined as the probability of the oc-
currence of a potentially damaging landslide in a given area and for a defined period of
time. Hazard assessment requires the estimation of the landslide magnitude, temporal
Copyright: © 2021 by the authors.
frequency and spatial location [7]. The affectable area and recurrence time are complicated
Licensee MDPI, Basel, Switzerland.
to predict because they are correlated to the complex natural processes that control the
This article is an open access article
mass movement and the difficulty in raising historical landslide data respectively. Surely it
distributed under the terms and could be easier to confine their predictive models to single slopes or very small areas for
conditions of the Creative Commons which detailed physical parameters and historical records can be found. On the other hand,
Attribution (CC BY) license (https:// the spatial distribution of potentially unstable areas can be easier to predict by assessing the
creativecommons.org/licenses/by/ likelihood of landslide occurrence in a region on the basis of the local ground conditions [8].
4.0/). Therefore, assessing landslide susceptibility represents the first step in evaluating landslide

Land 2021, 10, 162. https://doi.org/10.3390/land10020162 https://www.mdpi.com/journal/land

Land 2021, 10, 162 2 of 28

risk [9]: identification and mapping of areas where slope instability occurred in the past
or with similar physical-mechanical properties are useful to predict the spatial location of
future landslide initiation [10].
In this terms, landslide susceptibility assessment and mapping are an essential tool
in landslide risk management, supporting authorities, practitioners and decision makers
in the more appropriate and sustainable land planning and risk mitigation strategy de-
velopment, including the implementation of monitoring and warning systems [11–13].
In recent years, several predictive models have been proposed to assess and map the
landslide susceptibility, according to the scale and the aims of the analysis, the modelling
approaches and the adopted evaluation criteria [7,14,15]. They included both qualitative
and quantitative methods. The first type of approach usually returns a landslide suscepti-
bility zonation in terms of weighted indices and relative ranks (e.g., low, medium, high)
and are adopted for local scale and site-specific studies; the latter return an estimate of the
landslide occurrence as a numerical value. In the recent years, Geographical Information
System (GIS) technology has been largely used for landslide susceptibility assessing and
mapping, frequently combined with data detected by innovative techniques, e.g., satellite
remote sensing and light detection and ranging (LiDAR) images. GIS-based models allow
to manage big volumes of data, both in terms of file size and geographical scale, and to
perform a dynamic and on-going landslide susceptibility zonation, which represents an
essential requirement for a proper land-planning and risk mitigation [16–19].
According to several authors [7,15,20,21] methods proposed to assess landslide sus-
ceptibility can be classified into (i) qualitative geomorphological mapping and analysis of
landslide inventories, which depend on the ability and experience of the researcher and
the quality and completeness of the catalogues respectively [22–24]; (ii) semi-quantitative
heuristic or index-based approaches, which rely on the level of understanding of the geo-
morphological processes and the correlation between predisposing and triggering landslide
causal factors in order to rank and weight features responsible for instability, according
to their importance, expected or assumed, in triggering landslides [25–27]; (iii) physically
based methods, relying on the understanding of the physical laws which control the slope
stability, usually adopting a simple limit equilibrium model, e.g., infinite slope stability
model, or more complex ones [28–30]; (iv) statistical methods, based on the analysis of the
functional correlations between instability factors and the past/present landslide spatial
distribution, e.g., bivariate or multivariate analysis, linear and logistic regression, artificial
neural network, fuzzy logic, etc. [31–33].
Among heuristic approaches, the analytic hierarchy process (AHP) method has been
largely applied in literature for landslide susceptibility analysis [34–37]. AHP belongs to
the multicriteria techniques, which are largely applied in natural hazard management [38]
thanks to their capability in relating heterogeneous physical quantities [39]. Through a
matrix-based pair-wise comparison, this multiple criterion decision-making tool enables
one to analyze and compare the contribution of the different environmental factors involved
in landslide occurrence, which usually present a spatial and temporal variability.
In this paper, the semi-quantitative AHP method has been adopted to compute the
landslide susceptibility of the Portofino promontory, Northern Italy, which represents a
hilly mountain Mediterranean coastal area famous in the world for its great natural and
cultural landscapes, historically affected by shallow landslides and debris flows. Starting
from the catalogue of past landslides, nine natural and anthropic conditioning factors have
been selected, and different weight coefficients have been assigned to each of them and
each associated class adopting the AHP approach. Then, the weighted variables have been
combined and ranked into five different susceptibility levels. Finally, the resulting landslide
susceptibility map has been compared to the landslide susceptibility zonation which is
currently used by stakeholders in the regional and municipal land-planning and risk
management. Our study aims to highlight the difference between the two susceptibility
models and how a recurrent spatial analysis of landslide distribution at local scale is
essential to update the knowledge of the areas where shallow landslides and debris flows
Land 2021, 10, 162 3 of 28

may occur in the future, so that the most effective land management and successful
prevention of landslide risk can be provided. Further, including anthropogenic factors in
conditioning corresponds to the added effects of human activity on the Earth’s surface,
which considers man as a morphogenetic factor. The effects of human modification, that
may assume a very high importance locally, may result even in landslide generation. In
this sense the diffuse presence of abandoned man-made terraces may result in a possible
source of shallow landslides.

2. Materials and Methods

2.1. Study Area
The Portofino Promontory extends for 23 km2 in the eastern Liguria, Northern Italy,
between Genoa Gulf (W) and Tigullio Gulf (E) (Figure 1). It is world-famous for the
attractive landscapes and seascapes and its natural, historical and cultural heritage. Well-
known settlements of great value and seaside resorts stand along its coast, such as Camogli,
San Fruttuoso, Portofino, Paraggi and Santa Margherita Ligure. Because of the great
environmental and cultural values, the promontory is protected and included in the
Regional Natural Park of Portofino (18 km2 ) since 1935, including the seaward area that
became a marine reserve in 2001, when the boundaries of the protected area have been
redefined.

Figure 1. Location of the study area and geographical setting of the Portofino promontory.

The geological setting of the promontory is made by a sedimentary rock masses, with
conglomerate and flysch [40,41]. The Conglomerate of Portofino (Oligocene) crops out
in the southern sector, particularly along the steep slopes and high cliffs between Punta
Chiappa and Portofino, and secondarily along the south-eastern coastal stretches, between
Portofino and Punta Cervara. Conglomerate is made up of heterogeneous clastic elements,
mainly of marly limestone, sandstone, limestone and secondarily of other lithotypes, in
a sandy-limestone matrix. The formation features a fragile deformation tectonic, with
several fault and fracture systems oriented mainly NW-SE and NE-SW, both at the meso-
and macro-scale [42]. The Flysch of Mt. Antola (Cretaceous Sup. – Paleocene) is made
up of marly limestone layers ranging in thickness from decimeters to meters, with shales,
siltstones and calcarenites interlayers. It forms the central and norther sectors of the
Land 2021, 10, 162 4 of 28

promontory and widely crops out along the western slopes between Scogli Grossi and
Castellaro. The formation has been involved in a complex polyphase deformation tectonic,
both ductile and fragile, with large-scale folds with SSW vergence and WNW-ESE axis
orientation [43,44] which are well exposed along the cliffs.
Several fracture and direct fault systems, with different directions, control both ge-
ological and morphological settings of the promontory. The drainage pattern and the
coastline and ridge orientation are the result of the extensive tectonic activity that affected
the Ligurian continental margin during the Quaternary [45,46].
The peculiar geological and morphological setting is responsible for the widespread
instability processes, which affected large portions of the promontory, locally caused by
erosional processes and undercutting produced by running water and sea wave action
along slopes and cliffs. Most of the mass movements developed along the contact between
the formations, due to the different geomechanics behavior: the two largest landslides
are quite close to the tectonic contact, at Le Gave (eastern sector) and San Rocco (west-
ern sector) [47–49] (Figure 2A,B). Rockfalls and shallow landslides, including rapid and
destructive mud and debris flows, are involved frequently in the southern steep sectors
shaped in the conglomerate, whereas landslides characterized by different movement
types, including sliding and complex landslides, affected the gentler slopes shaped in the
marly limestone flysch [50,51] (Figure 2C,D). Recurrent falls and topples occurred along
the high cliffs made up of both conglomerate and flysch, also favored by the sea wave
action generally triggered by SW (Libeccio, dominant) and SE (Scirocco, prevailing) winds
depending on the orographic orientation (Figure 2E,F).
Mountains with high elevations (up 600 m a.s.l.) close up the coastline and high cliffs
(up to 200 m) characterize the southern and western sectors: slopes face mainly from SE
to W, with high to very high steepness values ranging from 50% to 75%, which locally
exceed 75% along the cliffs. Whereas the eastern and northern sectors of the promontory
are featured by gentle hills, with prevalently E- and NE- facing slopes and steepness values
ranging generally from 10% to 50%. A very small coastal floodplain (<0.5 km2 ) occupies
the final stretch of San Siro, Magistrato and Santa Barbara Creeks, where the town of Santa
Margherita Ligure stands (Figure 1).
Catchments have a high slope gradient, and they are generally small to very small
in size (less than 1 km2 ), except for few of them which extend up to 5 km2 (Acqua Morta
and San Siro creek, Gentile stream and the upper Boate stream basin). Consequently, their
hydrologic response to rainfall events is quite rapid, with time of concentration typically
less than 1–2 h. Watercourses are generally short, steep and incised, with a typical angular
pattern, as a result of the high relief and tectonics control. Streams are frequently dry,
particularly in summer months or during prolonged dry spell; however, during heavy
rainfall events, they can reach more considerable discharges with a large solid transport,
thereby evolving into rapid mud or debris flows.
Forests (950 ha) and shrub and/or herbaceous vegetation association (200 ha) occupy
large sectors of the promontory. Forested areas include broad-leaved forests (640 ha),
coniferous forest (124 ha) and mixed forest (186 ha). High cliffs and the steep S-facing
slopes have little or no vegetation (75 ha), followed by sparse vegetation (58 ha), bare
rocks (15 ha), whereas small pebbly beaches are in the little bays (<2 ha). The northern
and eastern hilly sectors are largely occupied by permanent crops (667 ha), including
olive groves (665 ha) and vineyards (<2 ha), heterogeneous agricultural areas (21 ha),
pastures (2 ha) and arable lands (<1 ha). Terraces characterize large sectors of the Portofino
promontory, totally modifying the former natural landscape: they are widespread both in
the current agricultural lands, largely represented by olive groves, in use or abandoned,
and in re-vegetated areas or in totally abandoned ones [51]. Artificial areas represent a very
small portion of the territory: urban fabric (247 ha), industrial, commercial and transport
units (32 ha) and artificial, non-agricultural vegetated areas (33 ha) occupy small stretched
of the coastal slopes and floodplain.
 destructive mud and debris flows, are involved frequently in the southern steep sectors
shaped in the conglomerate, whereas landslides characterized by different movement
types, including sliding and complex landslides, affected the gentler slopes shaped in the
marly limestone flysch [50,51] (Figure 2C,D). Recurrent falls and topples occurred along
Land 2021, 10, 162
the high cliffs made up of both conglomerate and flysch, also favored by the sea 5wave
of 28
action generally triggered by SW (Libeccio, dominant) and SE (Scirocco, prevailing) winds
depending on the orographic orientation (Figure 2E,F).

Figure 2. (A) Panoramic view of the western slope of the Portofino promontory below San Rocco
village historically affected by mud-debris flows, shallow landslides and rock falls (photo G. Stagni).
(B) A recent view of the relict coastal landslide at Le Gave from above, on the eastern slope. (C)
Source area of the mud-debris flows triggered on 26 July 2014, generated by the collapse of some
terraces. (D) Mud and debris at the outlet of the San Fruttuoso creek resulting from the collapse of
some terraces upstream due to intense rainfall event on 26 July 2014, which caused damage to some
tourist facilities in the historical village. (E) Rock fall involved conglomerate outcropping bedrock
at San Fruttuoso on 25 October 2016. (F) Damage on the road along the coastline between Santa
Margherita and Portofino on 27–28 October 2018, caused by a sea storm surge.

The complex geological, climate and environmental history and setting contribute
also to a significant spatial pedological variability in the soil horizons of the Portofino
promontory with six different Reference Soil Groups [52] and evidence of an extensive
ancient erosional surface and several paleosol features, suggesting the existence of different
tectonic and climate conditions that have been responsible for erosion phenomena [53].
The climate of the Portofino promontory is conditioned by the local orographic config-
uration: the presence of a mountain relief which exceed 600 m close to the sea (<1 km) and
the slopes aspects, determine peculiar insolation and exposure conditions to marine winds,
with a significant spatial climate variability. The climate is Mediterranean, with hot and dry
summers and mild winters in the southern, and changeable, rainy weather; whereas a mid-
hill zone, with more abundant precipitations and colder winters, characterizes the northern
sectors. Regime rainfall ranges depending on the orographic setting, with maximum rain-
fall in autumn, and minimum rainfall in summer [54]. Intense and short duration rainfalls
frequently occur in late summer or autumn months, from August to November, generated
by a typical atmospheric circulation over the Genoa Gulf, called Genoa Low [55,56] usually
associated with intense thunderstorms [57]: the resulting convective systems generate
localized and severe precipitation, which are frequently responsible for flash floods and
widespread shallow landslides and mud-debris flows [51,58,59].
Land 2021, 10, 162 6 of 28

2.2. Data and Research Methodology

A catalogue of rainfall-induced rapid and very rapid mass movements occurring in
the study area since 1910 has been compiled. In particular, gravitational processes with
high to very high velocity of the movement of variable volumes of materials triggered
by rainfall events, including shallow landslides, mud or debris flows and rock falls, were
considered. Information was gathered from different sources, including scientific papers,
technical reports, archives of local municipalities and newspaper articles [59]. Most of
the historical data derive from the inventory of sites historically affected by landslides
and floods events in Italy in the period 1918-1994 of the AVI Project (Hit Areas of Italy)
of GNDCI-CNR (National Group for Geo-hydrological Disaster Protection of National
Research Council) [60].
Using regional base maps at 1:10,000 scale (Table 1), landslides have been georefer-
enced within a GIS Geographical Information System open source software (QGIS 3.10).
Since areal information on landslide size has been difficult to retrieve due to the heteroge-
neous source types, punctual features have been adopted, which represents the highest
point of the landslide scarp. Simultaneously, for each landslide feature the following
information has been recorded [59]: (i) landslide type [5,61]; (ii) geographical location
and its spatial accuracy, from P1 (approximative location of slope failure within a buffer
area < 1 km2 ) to P3 (buffer area > 10 km2 ); (iii) data of the landslide occurrence; (iv) source
of landslide information and/or archive where bibliographic research was carried out.
In addition to the past rainfall-induced landslides inventory, the set of thematic maps
listed in Table 1 has been used to analyze the correlation between slope failures and both
natural and anthropic predisposing factors, and to generate the landslides susceptibility
map of the study area.

Table 1. Vector (V) and raster (R) data used. CTR, Regional Topographical Cartography; CORINE,
Coordination of Information on the Environment [62]; DTM, Digital Terrain Model; IFFI, Italian
Landslide Inventory [63].

Name Source Scala/Pixel Date Type

Administrative Unit Liguria Region 1:5000 2018 V
Aspect Liguria Region 1:10,000 2007 V
Buildings, Manufacts and Walls
Liguria Region 1:5000 2007 V
(CTR, 2nd Ed. 3D)
CORINE Land Use (II Level) Liguria Region 1:10,000 2018–2019 V
CTR Liguria Region 1:5000 1990–2006 R
DEM Liguria Region 5m 2016 R
Hydrographic Network and
Liguria Region 1:10,000 2019 V
Catchments
Landslide, Project IFFI Liguria Region 1:10,000 2014 V
Lithology Liguria Region 1:10,000 2017 V
Nature Reserve Borders Liguria Region 1:10,000 2019 V
Orthophotography Liguria Region 0.15 m 2004 R
Orthophotography Google Earth 2.5 m 2019 R
Pedological Map Rellini et al. [53] 1:10,000 2017 R
Road network, CTR 2nd Ed. 3D Liguria Region 1:5000 2007 V
Slope Liguria Region 1:10,000 2016 V
Springs Faccini et al. [64] 1:10,000 2018 V
Terraced areas Paliaga et al. [51] 1:5000 2020 V
Trail Network Liguria Region 1:25,000 2020 V

2.3. Landslide Conditioning Factors

Rainfall is commonly recognized as the major landslide causative elements [65]: shal-
low landslides and mud-debris flows triggered by very short, but intense or prolonged,
rainfalls can be particularly destructive due to their spatial distribution and rapid prop-
agation. However, several natural factors control the slope stability, including lithology,
Land 2021, 10, 162 7 of 28

topography, hydrographical network, land use and geomorphological processes can affect
the initiation of slope failures. Several researches highlighted the relationship between
shallow landslides occurrence and human activities [66–69]: deforestation for farming,
modifications in drainage patterns and slope profile due to artificial road cuts, fills or other
construction purposes, reduce the rainfall infiltration and increase the erosional processes
by surface run-off, predisposing slope to collapse.
In order to assess the landslide zonation of the Portofino promontory, the spatial re-
lationships among the rainfall-induced rapid mass movements that occurred in the study
area from 1910 to 2019 have been analyzed, and natural and anthropic landslide condition-
ing factors have been selected. In particular, the following nine thematic variables have
been selected: (i) lithology, (ii) slope aspect, (iii) slope acclivity, (iv) land use, (v) terraced
landscape, (vi) hydrographic elements distance, (vii) distance to man-made cuts elements,
(viii) distance to man-made structures and (ix) existing gravitational processes (Table 2).

Table 2. Landslide conditioning factors and correlated classes used in the analysis.

Conditioning Number of
Classes
Factor Classes
5 Heterogeneous clayey and sandy materials (Alluvial deposits)
Incoherent soils (Thick slope covers)
Lithology Heterogeneous materials of anthropic origin (Fills and
artificial deposits)
Marly limestone and marls (Flysch of Monte Antola)
Conglomerate (Conglomerate of Portofino)
9 North
North-east
East
South-east
Aspect South
South-west
West
North-west
Zenith
7 0–10%
11–20%
21–35%
Acclivity 36–50%
51–75%
76–100%
>100%
10 Urban fabric
Industrial, commercial and transport areas
Artificial, non-agricultural areas
Arable land
Permanent crops
Land use
Pastures
Heterogeneous agricultural areas
Forests
Shrubs and/or herbaceous vegetation association
Open spaces with little or no vegetation
Terraced area 2 Presence of terraces
4 Spring, distance < 10 m
Hydrographic Watercourses, distance < 10 m
elements Spring, distance > 10 m
Watercourses, distance > 10 m
Land 2021, 10, 162 8 of 28

Table 2. Cont.

Conditioning Number of
Classes
Factor Classes
4 Trial, distance < 5 m
Main road, distance < 5 m
Man-made cuts
Minor road, distance < 5 m
Man-made cuts, distance > 5 m
4 Buildings, distance < 10 m
Man-made Other manufacts, distance < 10 m
structures Retaining walls, distance < 10 m
Man-made structures, distance > 10 m
6 Active/reactivated/suspended
Dormant
Existing
Inactive/stabilized
Landslide
Area affected by widespread shallow landslides
(IFFI)
Assumed stable area, distance < 50 m
Assumed stable area, distance > 50 m

2.3.1. Lithology
It is generally known that geological and structural settings can affect the initiation
and development of shallow landslides [70,71]. We considered five classes derived from
the thematic regional map at 1:10,000 scale (Table 1) and based on the lithological features
(Figure 3A): (i) heterogeneous clayey and sandy materials, which form the alluvial deposits;
(ii) incoherent soils, including silt and clay with granular fraction and coarse soils, which
form the thick slope covers); (iii) heterogeneous materials of anthropic origin, which include
fills and artificial deposits; (iv) marly limestone and marls (Flysch of Monte Antola) and (v)
conglomerate (Conglomerate of Portofino), with eluvial and colluvial deposits.

2.3.2. Slope Aspect

Among morphological factors, aspect plays a relevant role in slope stability, inducing
different conditions of insolation, temperature, land cover, weather conditions and rainfall
exposure and, therefore, controlling important properties of the soils, e.g., temperature,
moisture and level of saturation [28,72]. Landslide occurrence is more common on south-
facing slopes than on north slopes, where failures occasionally occur due to the presence of
more vegetation covers that can increase soil strength and reduce water infiltration [73,74].
Adopting the regional thematic map at 1:10,000 scale (Table 1), we considered the nine
aspect classes shown in Figure 3B.

2.3.3. Slope Acclivity

Acclivity plays also a relevant role in the activation of instability processes [75,76]. We
derived the topographical information from the regional technical map at 1:10,000 scale
(Table 1), where slope steepness (in percentage) has been organized in seven classes, from 0
to 10% to >100% (Figure 3C).

2.3.4. Land Use

Several studies highlighted the relevant role of land use and changes in land use in
triggering shallow landslide [67,77], in addition to the other geological and morphome-
tric predisposing factors. We used the regional land use map at 1:10,000 scale based on
the CORINE nomenclature [62]. In particular, we considered ten land use categories of
second level (Figure 3D): (i) urban fabric, (ii) industrial, commercial and transport areas,
(iii) artificial, non-agricultural areas, (iv) arable land, (v) permanent crops, (vi) pastures,
(vii) heterogeneous agricultural areas, (viii) forests, (ix) shrubs and/or herbaceous vege-
tation association and (x) open spaces with little or no vegetation, including beaches and
bare rocks.
 predisposing factors. We used the regional land use map at 1:10,000 scale based on the
CORINE nomenclature [62]. In particular, we considered ten land use categories of second
level (Figure 3D): i) urban fabric, ii) industrial, commercial and transport areas, iii)
artificial, non-agricultural areas, iv) arable land, v) permanent crops, vi) pastures, vii)
Land 2021, 10, 162 heterogeneous agricultural areas, viii) forests, ix) shrubs and/or herbaceous vegetation
9 of 28
association and x) open spaces with little or no vegetation, including beaches and bare
rocks.

Figure 3. Shallow landslidesFigure 3. Shallow

predisposing landslides
factors. predisposing
(A) Lithology: factors. (A) Lithology:
1. Heterogeneous 1. Heterogeneous
clayey and sandy materialsclayey and
(Alluvial
sandy materials (Alluvial deposits); 2. Incoherent soils, including silt and clay with granular
deposits); 2. Incoherent soils, including silt and clay with granular fraction and coarse soils (Slope deposits); 3. Hetero-
fraction and coarse soils (Slope deposits); 3. Heterogeneous materials of anthropic origin (Fills and
geneous materials of anthropic origin (Fills and artificial deposits); 4. Marly limestone and marls (Flysch of Monte Antola);
5. Conglomerate (Conglomerate of Portofino). (B) Slope aspect: 1. North; 2. North-East; 3. East; 4. South-East; 5. South; 6.
South-West; 7. West; 8. North-West; 9. Zenith. (C) Slope steepness: 1. 0–10%; 2. 11–20%; 3. 21–35%; 4. 36–50%; 5. 51–75%;
6. 76–100%; 7. >101%. (D) Land use, based on the 2◦ level of CORINE nomenclature (European Environmental Agency,
1995): 1. Urban fabric; 2. Industrial, commercial and transport units; 3. Artificial, non-agricultural areas; 4. Arable land;
5. Permanent crops; 6. Pastures; 7. Heterogeneous agricultural areas; 8. Forests; 9. Shrubs and/or herbaceous vegetation
association; 10. Open spaces with little or no vegetation.

2.3.5. Terraced Landscape

Several studies have investigated the role of terraced landscapes in slope stability.
One hand, cultivated terraces control runoff processes on steep slopes, erosion and rainfall
infiltration, reducing the geo-hydrological risk; as a consequence, progressive terrace
abandonment may affect negatively the stability of the slope in response to very intense
rainfall events [78–81]. On the other hand, recent studies highlighted how the growth
of natural vegetation on abandoned terraces increases the slope stabilization and, at the
Land 2021, 10, 162 10 of 28

same time, how rainfall-induced shallow landslides affected both vegetated abandoned
and well-maintained cultivated terraces [51,82,83]. Unlike authors who did not compute
terraces in landslide susceptibility estimation [36], we included terraced landscape among
the anthropic variables that may influence the shallow landslides initiation. Terraced
surfaces in the study area are shown in Figure 4A: they have been detected carrying out
Land 2021, 10, x FOR PEER REVIEW the semi-automatic technique [51] for the terrace identification in the portion of Portofino
11 of 30
promontory within the Natural Park boundaries, using the raster data listed in Table 1.

Figure 4. Shallow landslidesFigure 4. Shallow

predisposing landslides
factors. predisposing
(A) Terraces: factors.
1. Terraced (A) 2.
area; Terraces: 1. Terraced
Non-terraced area. area; 2. Non-terraced
(B) Distance from
hydrographic network (buffer zone = 10 m): 1. Springs; 2. Watercourses. (C) Man-made cuts (buffer zone = 5 Watercourses.
area. (B) Distance from hydrographic network (buffer zone = 10 m): 1. Springs; 2. m): 1. Main
(C) Man-made cuts (buffer zone = 5 m): 1. Main road network; 2. Minor road network; 3. Trail
road network; 2. Minor road network; 3. Trail network. (D) Man-made structures (buffer zone = 10 m): 1. Buildings; 2.
network. (D) Man-made structures (buffer zone = 10 m): 1. Buildings; 2. Other manufacts; 3.
Other manufacts; 3. Retaining walls.
Retaining walls.

2.3.8.
2.3.6. Existing Gravitational
Hydrographic Elements Processes
Distance
We also considered the presence
Proximity to hydrographic networks, of pre-existing landslides, which
including watercourses may represent
and springs, may af-
areas with a potential higher proneness to slope instability. These instability
fect the slope stability [33,34,84]. Morphological modifications due to gully erosion processes
may
derived
influencefrom the regional
the removal catalogue materials
of incoherent of landslides
and at
the1:10,000 scale
activation of[63]
mass(Table 1), in which
movements, par-
landslides are mapped
ticularly along and[85];
steep slopes classified on thewatercourses
furthermore, basis of typemay of movement and stability
affect the slope state of
activity. In particular, we considered four classes: i) active/reactivated/suspended
landslides, ii) dormant landslides, iii) inactive/stabilized landslides, and iv) area affected
by widespread shallow landslides. In stable areas, certainly or supposed, we set two
further classes based on a buffer zone of 50 m from the boundaries of mapped landslides
Land 2021, 10, 162 11 of 28

by saturating the lower part of geomaterial and increasing the water level, particularly in
cases of permeable bedrock due to lithological or mechanical properties. We derived water-
courses and springs in the Portofino promontory from the regional thematic map at 1:10,000
scale (Table 1). To analyze their effects on shallow landslides triggering, we produced a
buffer map where streams and springs are buffered at distance of 10 m (Figure 4B).

2.3.7. Man-Made Cuts and Structures Distance

Similarly, anthropogenic changes to slope profile and runoff network induced by cut-
and-fill works can modify the stress-strain and drainage conditions, modifying the slope
equilibrium and increasing the possibility of shallow landslides activation [86–88]. Slope
cuts, such as roads or trails segments, frequently represent the source of failure [33,89,90];
but they can also act as a barrier or a preferential flow-path route, cutting the natural
drainage network. The influence of road and trail networks distance on shallow landslide
was analyzed, setting a buffer zone of 5 m around the linear elements (Figure 4C). In
particular, we considered both main and minor road features, extracted from the regional
topographical database, and the trail network of the Portofino promontory derived from
the correlate regional technical map (Table 1).
To analyze the influence of man-made structures distance on landslide occurrence,
we extracted buildings, manufacts and retaining walls from the regional topographical
database (Table 1) and buffered the features at a distance of 10 m (Figure 4D).

2.3.8. Existing Gravitational Processes

We also considered the presence of pre-existing landslides, which may represent
areas with a potential higher proneness to slope instability. These instability processes
derived from the regional catalogue of landslides at 1:10,000 scale [63] (Table 1), in which
landslides are mapped and classified on the basis of type of movement and state of activity.
In particular, we considered four classes: (i) active/reactivated/suspended landslides, (ii)
dormant landslides, (iii) inactive/stabilized landslides, and (iv) area affected by widespread
Land 2021, 10, x FOR PEER REVIEW shallow landslides. In stable areas, certainly or supposed, we set two further classes12based
of 30
on a buffer zone of 50 m from the boundaries of mapped landslides (Figure 5A).

FigureIFFI
Figure 5. (A) Existing landslides, 5. (A) Existing
Project landslides,
(Regione IFFI
Liguria, Project
2014): (Regione Liguria, 2014): 1. landslide;
1. Active/reactivate/pendent Active/reactivate/pendent
2. Dormant
landslide; 2. Dormant landslide; 3. Inactive/stabilised landslide; 4. Area affected by widespread
landslide; 3. Inactive/stabilised landslide; 4. Area affected by widespread shallow landslides; 5. Buffer zone (50 m). (B)
shallow landslides; 5. Buffer zone (50 m). (B) Spatial distribution of gathered rainfall-induced
Spatial distribution of gathered rainfall-induced shallow landslides over the 1910–2019 period: 1. Complete set. 2. Training
shallow landslides over the 1910-2019 period: 1. Complete set. 2. Training set. 3. Test set.
set. 3. Test set.
2.4. Analytic Hierarchy Process (AHP)
To assess the landslide susceptibility of the Portofino promontory combining all the
variables, we adopted the Analytic Hierarchy Processes (AHP) developed by Saaty [91],
a semi-quantitative multi-criteria decision-making approach which enables to compute
rank and weight of each criterion by a pair-wise comparison.
Land 2021, 10, 162 12 of 28

2.4. Analytic Hierarchy Process (AHP)

To assess the landslide susceptibility of the Portofino promontory combining all the
variables, we adopted the Analytic Hierarchy Processes (AHP) developed by Saaty [91], a
semi-quantitative multi-criteria decision-making approach which enables to compute rank
and weight of each criterion by a pair-wise comparison.
This approach includes several steps: (i) break a composite unstructured problem
down into its component factors n and arrange them in a hierarchic order; (ii) assign
numerical values according to the subjective relevance of each factor to determine its
relative importance by a matrix-based pair-wise comparison

A = ka(i,j) k, (1)

where i, j = (1, 2, . . . , n); (iii) synthesize the rating to determine the priority ω n , i.e., the
weight, to be assigned to each factor computing the normalized principal eigenvector λmax ,
which corresponds to the largest eigenvalue [92,93]. In the pair-wise comparison, a scale of
preference alternatives is used to associate a score based on the subjective judgment on the
relative importance of each factor against every other one (Table 3).

Table 3. Scale of preference between two criteria used in the pair-wise comparison in AHP [88].

Scale Degree of Preference Description

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

In this study, the nine considered variables influencing shallow landslides represent
the component factors of the unstructured composite problem required by the method. To
analyze the correlations between the conditioning factors and the landslides that occurred
in the study area and arrange them in a hierarchic order, we firstly performed a simple
frequency distribution analysis for each conditioning factor and for the different classes
within each of them (Table 2). For this purpose, according to Chung and Fabbri [94] and
Carrara et al. [95], we considered a portion of the gathered landslide inventory to evaluate
the proneness to shallow landslide (training set), using the remaining landslides data to
validate the resulting susceptibility model (test set) (Figure 5B). The training set has been
defined by selecting randomly [9] 80% of the georeferenced landslides with a geographical
accuracy <10 km2 (P1 and P2).
Next, with respect to their impact on slope stability, we performed the pair-wise
comparison for each conditioning factor and for the different classes within each factor
using the AHP Excel template [96]. The template, which works under Excel version MS
Excel 2013, can solve and combine the matrix of pair-wise comparisons of a maximum
number of ten criteria computed by a maximum number of twenty experts. In this work,
we used this approach to combine AHP results computed by five participants. The AHP
Excel template includes (i) twenty input worksheets for pair-wise comparisons, where
priorities are calculated using the row geometric mean method (RGMM); (ii) a sheet for the
consolidation of all judgments, where weights given in the input sheets by individual par-
Land 2021, 10, 162 13 of 28

ticipant are aggregated using the Weighted Geometric Mean (WGM); and a (iii) summary
sheet to display the consolidated final results for all the experts.
Final results include (i) weights of all considered criteria and associated errors (in %); (ii)
principal or largest eigenvalue λmax of the matrix, and (iii) a modified consistency ratio CR
as proposed by Alonso and Lamata [97], calculated in all input sheets and in the summary
sheets. The consistency ratio CR is an index ranging from 0 to 1, used to determine the
degree of matrix consistency; it has been defined by Saaty [92] as the ratio between the
calculated consistency index of the matrix CI and a random consistency index RI.
The consistency index CI estimates the consistency of weights and ratings and it is
calculated based on Saaty’s [98] expression:

CI = (λmax − n)/(n − 1), (2)

where λmax is the largest eigenvalue and n is the order of the matrix, corresponding to the
number of parameters.
The random consistency index RI, reported by Saaty [92] as the average of the resulting
consistency index depending on the order of the matrix, in the AHP Excel Template
adopted in the present study is calculated using the Alonso and Lamata linear fit [97],
which estimates the RI as the best adjustment fit of the values of λmax (n):

λmax (n) = 2.7699 n − 4.3513, (3)

where λmax is the mean of λmax .

The CR is therefore calculated by the following expression:

CR = (λmax − n)/(2.7699 n − 4.3515 − n) (4)

Value of CR smaller than or equal to 0.1 (10%) is acceptable and, consequently, the
matrix is consistent.
An AHP consensus indicator S* is also calculated to quantify the consensus of the
group, i.e., to estimate the agreement on the outcoming priorities between all the experts
and it can be interpreted as a measure of overlap between priorities of the group members.
This indicator is calculated using the partitioning diversity in Alpha and Beta diversity [99]
derived from the Shannon entropy [100]. The consensus indicator S* ranges from 0%
(no consensus between experts) to 100% (full consensus between experts): values below
50% indicate a very low consensus, i.e., a high diversity of judgments, whereas values
ranging from 80% to 90% indicate a high overlap of priorities and an excellent agreement
of judgments from the group members.

2.5. Landslide Susceptibility Map

Once coherent weights have been assigned to each landslide causal factor and to
each class within conditioning factor performing the AHP approach, all thematic vector
layers have been converted into grid format with a 5 × 5 m cell size using the GRASS
7 (Geographic Resources Analysis Support System) native plugin in QGIS. Next, the
reclassification of all grid data by weight values has been performed using the SAGA
(System for Automated Geoscientific Analysis) reclassify values algorithm in QGIS.
A raster analysis has been performed to construct the landslide susceptibility map
(LSM) using the Raster Calculator tool in QGIS. All the weighted predisposing factors and
their classes have been combined using a weighted linear sum and then normalized by
scaling values between 0 and 1 using the maximum value for the calculated variable:

LSM = (∑i=1 n wij Wj )/(∑i=1 n wij Wj )max , (5)

where wij is the weight factors of the class i in the conditioning factors j, Wj is the weight of
the landslide conditioning factor j. Lastly, values resulting from the raster analysis have
Land 2021, 10, 162 14 of 28

been classified into 5 classes (low, moderate, high and very high) based on the histogram
classification to determine the interval of each landslide susceptibility class in the map.

3. Results
We identified 85 rainfall events and 114 landslides in the 1910–2019 period. More than
50% of the observed instability processes involved the western and south-western sectors
of the promontory, within the Camogli municipality, followed by Santa Margherita Ligure
(30%) and Portofino (16%) (Figure 6A). Shallow landslides (43%) and rock falls (30%) are
the most representative type of rainfall-induced rapid mass movements. Debris and mud
Land 2021, 10, x FOR PEER REVIEW 15 of 30
flows occur with less frequency (<10%) (Figure 6B).

Figure 6. (A) Distribution of rainfall-induced shallow landslides in the Portofino promontory:

Figure 6. (A) Distribution of rainfall-induced
CAM, Camogli shallow
municipality; POR, Portofino landslides
municipality; in Santa
SML, the Portofino promontory: CAM,
Margherita Ligure
Camogli municipality; POR,
municipality (forPortofino
location, see municipality;
Figure 1). (B) TypeSML, Santa Margherita
of rainfall-induced Ligure
shallow landslides municipality
within the
Portofino promontory: DF, debris flow; MF, mud flow; ns, not specified; RF, rock fall; Sh, shallow
(for location, see landslide.
Figure 1). (B) Type of rainfall-induced shallow landslides within the Portofino
promontory: DF, debris flow; MF, mud flow; ns, not specified; RF, rock fall; Sh, shallow landslide.
Results of the landslides spatial analysis show that slope failures occurred mainly in
marly
Results of the limestone with
landslides shale interlayers
spatial analysis(48%) showandthat
conglomerate (41%), whereas
slope failures incoherent
occurred mainly in
soils and heterogeneous materials of anthropic origin are involved less frequently (8% and
marly limestone3% with shale interlayers (48%) and conglomerate (41%),
respectively). With respect to the morphometric parameters, mass movements whereas incoherent
soils and heterogeneous
induced by materials
rainfall affectedof anthropic
mainly slopesorigin are involved
of east (21%), north-westless
(20%)frequently
and west (18%) (8% and
3% respectively).direction; south-west
With respect (13%)morphometric
to the and north-east (11%) are also relevant
parameters, slope directions,
mass movements induced
whereas north and south-east directions (5% respectively) are no representative.
by rainfall affected mainly slopes of east (21%), north-west (20%) and west
Landslides triggered mainly on slopes with high and very high steepness values (70%),
(18%) direction;
south-west (13%) andthan
higher north-east
50%. Most(11%) are also
of instability relevant
processes slope directions,
developed within vegetatedwhereas
areas, north
and south-east characterized
directions by (5%mixed forests (26%), olive
respectively) are groves (26%) and broad-leaved
no representative. forests (20%).
Landslides triggered
Regarding urbanized areas, landslides have been detected largely on slopes with a
mainly on slopes with high and very high steepness values (70%), higher
discontinuous urban fabric (15%). Only 1% of gravitational processes involved bare rocks.
than 50%. Most
of instability processes developed within vegetated areas, characterized
Half of the observed landslides affected terraced slopes (50%), made of dry-stone walls by mixed forests
(26%), olive groves (26%)
terraces or grassyand broad-leaved
embankments, forests (20%).
both well-maintained Regarding urbanized areas,
and abandoned.
With respect to the existing landslides mapped in the regional master plans, we
landslides have been detected largely on slopes with a discontinuous urban fabric (15%).
found that most of the rainfall-induced failures (80%) involved slopes where no landslides
Only 1% of gravitational processes
have been previously involved
detected. bareof rocks.
Only in 20% Half
cases, they ofwith
match theexisting
observed landslides
landslides,
affected terraced slopesas(50%),
classified made of dry-stone(16%)
active/reactivated/suspended walls terraces
and dormantor grassy
(4%); embankments,
this percentage
increasesand
both well-maintained to 45% if we consider a buffer of 50 m around the landslide features. Similarly,
abandoned.
in few cases landslides occurred within 10 m from watercourses (11%) or within 5 m from
With respect to the existing
man-made landslides
cuts and structures, mapped
including main in the(9%),
roads regional master
trails (11%), plans,
buildings we found
(21%),
that most of themanufacts
rainfall-induced failures
(7%) and retaining (80%)
walls (27%).involved slopes where no landslides have
Among the
been previously detected. Only 114 in
landslides,
20% of we considered
cases, 102 processes
they match for which landslides,
with existing geographical classi-
location was known with a high or medium spatial accuracy (P1 and P2). The training set
fied as active/reactivated/suspended (16%) and dormant (4%); this percentage increases
used to assess the landslide susceptibility consists of 81 landslides.
to 45% if we consider As aproposed
buffer of by50 themAHP
around the the
method, landslide
pairwisefeatures.
comparison Similarly,
matrix hasinbeenfew cases
landslides occurred within
constructed both10 m from
between watercourses
the nine (11%)
landslide causal factorsor
andwithin
between5the mclasses
fromwithin
man-made
each of them. In order to assess their impact on slope stability, each variable has been rated
cuts and structures, including main roads (9%), trails (11%), buildings (21%), manufacts
against every other by assigning a relative dominant value between 1 and 9 to the
(7%) and retaining
intersecting (27%).
walls cell based on the results of the frequency analysis performed using the
Among therandom
114 landslides,
selected landslide we considered 102 4processes
training set. Table summarizesfor which principal
normalized geographical
eigenvectors and associated errors for conditioning factors. Complete pairwise
location was known with a high or medium spatial accuracy (P1 and P2). The training set
comparison matrixes for causal factors and for the classes within each landslide
used to assess the landslide
conditioning susceptibility
factor consists
are shown in Appendix A,of 81 landslides.
Appendix B and Appendix C.
As proposed by the AHP method, the pairwise comparison matrix has been constructed
both between the nine landslide causal factors and between the classes within each of them.
In order to assess their impact on slope stability, each variable has been rated against every
other by assigning a relative dominant value between 1 and 9 to the intersecting cell based
Land 2021, 10, 162 15 of 28

on the results of the frequency analysis performed using the random selected landslide
training set. Table 4 summarizes normalized principal eigenvectors and associated errors
for conditioning factors. Complete pairwise comparison matrixes for causal factors and for
the classes within each landslide conditioning factor are shown in Appendices A–C.

Table 4. Summary of AHP analysis [96] computed by five participants: normalized principal
eigenvector value and associated errors for landslide conditioning factors.

Number of Normalized Principal

Factor Error
Criteria Eigenvector
Lithology 5 0.155 0.076
Aspect 9 0.090 0.040
Acclivity 7 0.311 0.107
Land use 10 0.197 0.093
Terraced area 2 0.064 0.033
Distance hydrographic elements 4 0.034 0.015
Distance man-made cuts 4 0.031 0.015
Distance man-made structures 4 0.055 0.003
Existing landslides 6 0.063 0.024

Calculated weights for the landslide conditioning factors range between 0.031 and
0.311: getting the highest value, slope acclivity represents the most relevant factor in the
initiation of shallow landslides on the Portofino promontory, followed by land use (0.197)
and lithology (0.155). Contrariwise, distance from hydrographic elements and man-made
cuts slightly influence the landslide occurrence, weighting 0.034 and 0.031 respectively.
By considering the different classes within each conditioning factor (Appendices B and C),
the lithological features more predisposing to instability are limestone flysch (0.507) and
conglomerate (0.308), whereas low weights have resulted for incoherent soils (0.04) and
heterogeneous materials of anthropic origin (0.022). Regarding the morphometric condi-
tioning factors, the highest weights have been assigned to slopes facing NW (0.285) and
E (0.248) respectively and slope acclivity values ranging between 51% and 75% (0.331);
medium weights to slope oriented to W (0.144) and SW (0.111) and steepness range values
ranging from 76% to 100% (0.221) and higher 100% (0.202) respectively; lowest weights to
the remaining morphometric classes. For land use, forest (0.358) is the more predisposing
class to shallow landslides initiation, followed by permanent crops (0.204) and urban fabric
(0.150); similarly, presence of terraces greatly influences the landslides occurrence (0.739).
Weights result proportionally to the distance of landslides from the hydrographic network
(0.394), man-made cuts (0.672) and anthropic structures (0.540): the farthest buffer zones had
the higher assigned weights. Regarding pre-existing landslides, the highest scores have been
assigned to area where no former gravitation processes have been mapped within the buffer
zone higher (0.423) and lower (0.300) than 50 m from the existing landslide boundaries.
Consistency of the weights and rating calculated for each conditioning factors and the
agreement on the outcoming priorities between all the participants are shown in Table 5.
Values of the Consistency Ratio index (CR) are smaller than 10%, confirming that all the
constructed matrixes are consistent: excepting for lithology (9.2%), CR values range from
0.0% (presence of terraced areas) to 5.2% (presence of existing landslides). Consensus
indicator (S*) values reported in Table 5, ranging from 94.6% and 99.0%, show a very good
consensus between the members of the research team.
Combining weightage of conditioning factors and their classes, we obtained the
landslide susceptibility map of the Portofino promontory. We normalized the resulting
LSM values, which ranged from 0.079 to 0.401, by scaling values between 0 and 1. In
order to determine the class intervals in the obtained map, we took into consideration
the classification systems commonly applied in landslide susceptibility zonation: natural
breaks, quantiles and equal intervals [101]. On the basis of the raster map histogram, which
is multimodal and shows empty class intervals, we adopted the natural breaks method
to divide the LSM values into five classes shown in Table 6. In this case, the quantile
Land 2021, 10, 162 16 of 28

classification is not appropriate because data are not linearly distributed; whereas the
lowest susceptibility class is too emphasized relative to others using equal intervals.

Table 5. Number of criteria considered for each landslide conditioning factor, Eigenvalue λ, consis-
tency ratio, CR [97] and consensus indicator, S* [96].

Number of CR S*
Factors Eigenvalue λ
Criteria (%) (%)
Lithology 5 5.413 9.2 99.0
Aspect 9 9.480 4.1 97.9
Acclivity 7 7.296 3.7 95.6
Land use 10 10.330 2.5 94.9
Terraced areas 2 2.000 0.0 96.7
Distance hydrographic elements 4 4.068 2.5 96.9
Distance man-made cuts 4 4.035 1.3 94.6
Distance man-made structures 4 4.064 2.3 96.5
Existing landslides 6 6.329 5.2 98.9

Table 6. Landslide susceptibility classes and correlated LSM range value.

Class LSM Range

Very low 0.00–0.40
Low 0.41–0.55
Medium 0.56–0.65
High 0.66–0.75
Very high 0.76–1.00

The resulting landslide susceptibility map for the Portofino promontory is presented
in Figure 7A. Most of the territory (74%) results prone to shallow landslide occurrence: the
highest extension percentage (30%) falls into moderate landslide susceptibility class, followed
by high (26%) and very high (18%) susceptibility classes (Figure 7B). Otherwise, a small portion
of the study area (26%) has been classified with low and very low landslide susceptibility.
We observed that areas with the highest proneness to rapid and very rapid mass move-
ments occurrence (class = 5) comprise primarily the southern sectors of the promontory,
where bedrock mainly consists of flysch (57%) and, secondarily, of conglomerate (30%), and
slopes have a prevalent E (21%) and SE (14%) exposure, with high and very high steepness
values (43%) comprised between 51% and higher than 101%. Sectors with high to very high
landslide susceptibility are largely covered by forests (45%), permanent crops (34%) and
shrubs or other natural vegetation (11%), with large areas characterized by outcropping
bedrock, particularly along the high cliffs. Furthermore, they show the lowest interaction
with anthropic elements, including roads (12%), buildings (6%), man-made structures (5%)
and terraces (7%), and with pre-existing gravitational processes (1%).
Validation of the landslide susceptibility map has been performed on the basis of the
test set consisting of 20 randomly selected landslide locations [94]. The distribution of
landslides in the five LSM classes represents the qualitative assessment of the susceptibility
map: we found that most of the landslides (65%) fell within the very high and the high
susceptibility classes (35% and 30% respectively), confirming the strong connection between
the occurrence of rainfall-induced rapid mass movements and the produced susceptibility
zonation. A quantitative assessment of the predictive accuracy of the map has been
computed using the Receiver Operating Characteristic curve (ROC) and the Area Under
Curve (AUC) [35,102]. For this purpose, we plotted the true positive rate (TPR), i.e., the
correctly predicted events, opposite to the false positive rate (FPR), i.e., the falsely predicted
events, by varying the cut-off value. The prediction curve shows that the AUC value was
0.73 (Figure 8). According to Sewts [103], the quantitative relationship between the AUC
value and the accuracy of the predictive model is classified as weak (0.5–0.6), moderate
(0.6–0.7), good (0.7–0.8), very good (0.8–0.9) and excellent (0.9–1.0). Therefore, the proposed
Land 2021, 10, 162 17 of 28

Land 2021, landslide

10, x FOR PEERsusceptibility
REVIEWmap had a good prediction accuracy in rapid mass movements 18 of 30
occurrence in the study area, when considering all the considered causative factors.

Land 2021, 10, x FOR PEER REVIEW 19 of 30

landslides in the five LSM classes represents the qualitative assessment of the
susceptibility map: we found that most of the landslides (65%) fell within the very high
and the high susceptibility classes (35% and 30% respectively), confirming the strong
connection between the occurrence of rainfall-induced rapid mass movements and the
produced susceptibility zonation. A quantitative assessment of the predictive accuracy of
the map has been computed using the Receiver Operating Characteristic curve (ROC) and
the Area Under Curve (AUC) [35,102]. For this purpose, we plotted the true positive rate
(TPR), i.e., the correctly predicted events, opposite to the false positive rate (FPR), i.e., the
falsely predicted events, by varying the cut-off value. The prediction curve shows that the
AUC value was 0.73 (Figure 8). According to Sewts [103], the quantitative relationship
between the AUC value and the accuracy of the predictive model is classified as weak (0.5
– 0.6), moderate (0.6 - 0.7), good (0.7 – 0.8), very good (0.8 – 0.9) and excellent (0.9 – 1.0).
Therefore, the proposed landslide susceptibility map had a good prediction accuracy in
rapid mass movements occurrence in the study area, when considering all the considered
Figure 7. (A) Landslide susceptibility map of the Portofino promontory. (B) Histogram of the
causative
Figure 7. factors.
(A) Landslide susceptibility map of the Portofino promontory. (B) Histogram of the
percentage of areas included in the five susceptibility classes.
percentage of areas included in the five susceptibility classes.
We observed that areas with the highest proneness to rapid and very rapid mass
movements occurrence (class=5) comprise primarily the southern sectors of the
promontory, where bedrock mainly consists of flysch (57%) and, secondarily, of
conglomerate (30%), and slopes have a prevalent E (21%) and SE (14%) exposure, with
high and very high steepness values (43%) comprised between 51% and higher than 101%.
Sectors with high to very high landslide susceptibility are largely covered by forests (45%),
permanent crops (34%) and shrubs or other natural vegetation (11%), with large areas
characterized by outcropping bedrock, particularly along the high cliffs. Furthermore,
they show the lowest interaction with anthropic elements, including roads (12%),
buildings (6%), man-made structures (5%) and terraces (7%), and with pre-existing
gravitational processes (1%).
Validation of the landslide susceptibility map has been performed on the basis of the
test set consisting of 20 randomly selected landslide locations [94]. The distribution of

Figure 8.
Figure 8. Predictive
Predictiveaccuracy of the
accuracy proposed
of the landslide
proposed susceptibility
landslide model model
susceptibility using the ROCthe
using curve.
ROC curve.

4. Discussion
The landslide susceptibility map obtained through the AHP semi-quantitative
approach (Figure 9A) represents a great tool for risk mitigation planning. The proposed
methodology, already applied in similar geographical and geological context, combines
Land 2021, 10, x FOR PEER REVIEW 20 of 30
Land 2021, 10, 162 18 of 28

lithology where a statistical approach is used, considering the proportion of in landslide

lithotype. Maps are summed up, and after normalization, susceptibility classes are
4. Discussion
assigned.TheThen, the methodology
landslide is highly
susceptibility map affected
obtained by subjectivity
through and strongly relies
the AHP semi-quantitative ap- on
the presence of active and inactive landslides. Conditioning factors
proach (Figure 9A) represents a great tool for risk mitigation planning. The proposedat local scale,
including man-made landforms, are not included in the assessment
methodology, already applied in similar geographical and geological context, combines of landslide
susceptibility. Furthermore,
an objective and quantitativeareas wherewith
component active, dormant
a degree or inactive/stabilized
of subjectivity landslide,
that arises from the
deep seated
experts’ gravitational
judgement, whichslope deformation,
are properly widespread
mediated through the active
AHPormethod.
dormant instability
Then, the
phenomena
subjectivityand shallow
level, althoughlandslides
reduced are detected, are
in importance, automatically
allows classified
one to introduce as high or
the experts’
opinion
very highinsusceptibility
the decision-making
zones. process. The result
The obtained map is aissusceptibility
classed in 5map that combines
increasing levels of
the experts’ opinions
susceptibility, and the
named from objective
Pg0 to Pg4 record
(Figureof9B).
past events.

Figure(A)
Figure 9. Comparison between: 9. the
Comparison between:susceptibility
proposed landslide (A) the proposed landslide
map, and (B) thesusceptibility map, andzonation
landslide susceptibility (B) the
landslide
currently adopted by the regional susceptibility
land zonation currently
and risk management adopted
plan. Black by the regional
dots represent land and
rainfall induced risk management
landslides which
occurred after 2013. plan. Black dots represent rainfall induced landslides which occurred after 2013.

We observed
Actually, that areas
the river with very high
basin management planlandslides susceptibility
(defined Basin Master Plan) overlap effectively
[104] adopts a
different methodology to obtain the landslide susceptibility map (Figure
with existing active mapped landslides; similarly, slopes with high susceptibility are 9B). The methodol-
ogy, which
largely derives
affected from a specific
by existing dormanttechnical regulation,
or inactive includes some conditioning factors
landslide.
at the catchment’s scale: lithology, slope steepness,
The comparison between the regional susceptibility land use, hydrological
zonation and effectiveness,
the spatial
soil cover and related granulometry and the presence of active or inactive
distribution of rainfall-induced shallow landslides occurred in the Portofino promontory landslides. Some
other worsening factors are then included, e.g., lithological contact, fault, erosional talweg
after 2013, which is the year of publication of the regional map, shows that most of them
channel, edge of fluvial erosion scarp, terrace edge, slope angle discontinuity. Weights
affected slopes classified as medium (Pg2) susceptibility. This result highlights the
within every factor are assigned subjectively, apart from lithology where a statistical ap-
unreliability of the previous model to map landslide susceptibility for predicting areas
proach is used, considering the proportion of in landslide lithotype. Maps are summed
where
up, andnew slope
after failures may
normalization, occur, asclasses
susceptibility it probably relies Then,
are assigned. too much on the existing
the methodology is
phenomena in terms of their geometrical features, and less on the conditioning
highly affected by subjectivity and strongly relies on the presence of active and inactive factors.
The differences
landslides. Conditioning between theatprevious
factors model
local scale, and the
including proposed
man-made one (Figure
landforms, 9) are
are not
particularly
included inrelevant in the of
the assessment southern
landslidepart of the area,Furthermore,
susceptibility. whose lithologyareasiswhere
conglomerate,
active,
which is characterized
dormant by strong landslide,
or inactive/stabilized steepness.deepThis seated
factor, gravitational
in the studiedslope
area,deformation,
is probably the
widespread
crucial one but active
it is or dormantaccompanied
primarily instability phenomena and shallow
by man-made landslides
landforms are detected,
and hydrographical
are automatically classified as high or very high susceptibility
network, and then by the others. The proposed model probably underestimates zones. The obtained map is the
classed in 5 increasing levels of susceptibility, named from Pg0 to Pg4 (Figure
presence of existing large landslides, but this apparent limitation may be easily overcome, 9B).
adopting the same method of the actual in-use model, which automatically assigns the
highest class to active landslide zones (Figure 10). In fact, it is surely true that active
landslides represent risk elements themselves and may result, in some local portions, as a
possible source of rapid mass movements.
 Land 2021, 10, 162 19 of 28

We observed that areas with very high landslides susceptibility overlap effectively
with existing active mapped landslides; similarly, slopes with high susceptibility are largely
affected by existing dormant or inactive landslide.
The comparison between the regional susceptibility zonation and the spatial distri-
bution of rainfall-induced shallow landslides occurred in the Portofino promontory after
2013, which is the year of publication of the regional map, shows that most of them affected
slopes classified as medium (Pg2) susceptibility. This result highlights the unreliability of
the previous model to map landslide susceptibility for predicting areas where new slope
failures may occur, as it probably relies too much on the existing phenomena in terms of
their geometrical features, and less on the conditioning factors.
The differences between the previous model and the proposed one (Figure 9) are
particularly relevant in the southern part of the area, whose lithology is conglomerate,
which is characterized by strong steepness. This factor, in the studied area, is probably the
crucial one but it is primarily accompanied by man-made landforms and hydrographical
network, and then by the others. The proposed model probably underestimates the
presence of existing large landslides, but this apparent limitation may be easily overcome,
adopting the same method of the actual in-use model, which automatically assigns the
highest class to active landslide zones (Figure 10). In fact, it is surely true that active
Land 2021, 10, x FOR PEER REVIEW
landslides represent risk elements themselves and may result, in some local portions, as 21 a of 30
possible source of rapid mass movements.

Figure10.
Figure 10.The
Theobtained
obtained susceptibility
susceptibility mapmap
withwith automatic
automatic assignment
assignment of the highest
of the highest class to class to
existing
existing
active active landslides
landslides zones and zones and areas
areas affected affected by widespread
by widespread shallow landslides.
shallow landslides.

The
Thecomparison
comparisonbetween
between thethe
proposed susceptibility
proposed map (Figure
susceptibility 10) and10)
map (Figure the and
re- the
gional susceptibility zonation (Figure 9B) has been performed through Cohen’s
regional susceptibility zonation (Figure 9B) has been performed through Cohen’s Kappa Kappa
calculation
calculation[105,106],
[105,106],using
using thethe
Map
Mapcomparison
comparisonkit software, version
kit software, 3.2.3 3.2.3
version [107]. [107].
The The
obtained Kappa value was 0.108, and the correct fraction was 0.308, showing a general
obtained Kappa value was 0.108, and the correct fraction was 0.308, showing a general
slight concordance between the two maps. Results per susceptibility class are shown in
slight concordance between the two maps. Results per susceptibility class are shown in
Table 7: medium and high classes displayed the lowest and no concordance, respectively,
Table 7: medium and high classes displayed the lowest and no concordance, respectively,
while the very low one presented a relatively higher degree of concordance, confirming
while
the the very
general resultlow one
even presented a relatively
if differentiating between higher
classes.degree of concordance,
The lowest susceptibilityconfirming
class
the general result even if differentiating between classes. The lowest susceptibility class
corresponded substantially with the lower slope gradient areas, that is the lower values
of the more critical conditioning factor. On the other hand, the weak concordance in the
three higher susceptibility classes highlights the strong differentiation between the two
maps where landslide triggering probability is higher.
Land 2021, 10, 162 20 of 28

corresponded substantially with the lower slope gradient areas, that is the lower values of
the more critical conditioning factor. On the other hand, the weak concordance in the three
higher susceptibility classes highlights the strong differentiation between the two maps
where landslide triggering probability is higher.

Table 7. Kappa per susceptibility class map comparison between regional zonation and the proposed
one.

Class Very Low Low Medium High Very High

Kappa 0.322 0.185 0.044 −0.014 0.173

Then, the proposed susceptibility map (Figure 10), modifying landslides area zonation,
may be intended as a more effective and efficient tool to support risk mitigation strategies
and planning. More accurate knowledge of where a future possible landslide may occur
allows us to eventually reduce the importance of critical man-induced factors and to
promote proper prevention measures that are crucial for reducing damage and even
victims [39,108,109].

5. Conclusions
The research allowed us to assess a landslide susceptibility map for the Portofino
promontory, based on over 110 years of rapid mass movements inventory and experts’
opinions, to apply a semi-quantitative AHP method. Then, the methodology combines the
quantitative approach with an expert-based view. The analysis of nine conditioning factors,
which includes anthropogenic landforms related ones, results in a susceptibility map that
highlights areas where possible future landslides may occur with a reliability that appears
higher than the one of the actual officially adopted map. The way the proposed map has
been compiled seems more oriented towards the possible future landslide scenario, rather
than weighting the existing landslides with higher importance.
Then, the proposed map results as a possible decision support tool to implement risk
mitigation strategies and to better apply land use planning in an area that is periodically hit
by intense rain events that often induce rapid mass movements. The combination of those
types of landslides and flash flood often occurs, causing the culverts saturation in urban
and peri-urban areas and resulting in large damage. The RECONECT EU funded project is
actually adopting risk mitigation strategies for slope stabilization in the studied area and a
susceptibility map is a crucial decision support tool for interventions planning [110].
Finally, the methodology allows us to modify factors, including new ones or excluding
others, in order to localize features, and it may then be adopted in different conditions or
geographical contexts.

Author Contributions: Conceptualization, A.R. and G.P.; methodology, A.R. and G.P.; software A.R.
and G.P.; validation, A.R., G.P. and F.F.; investigation, A.R.; resources, A.R. and G.P.; data curation, F.F.,
F.L. and L.T.; writing—original draft preparation, A.R. and G.P.; writing—review and editing, A.R.,
F.F. and L.T.; visualization, A.R., G.P., F.L. and L.T.; supervision, G.P. and L.T.; project administration,
F.L. and L.T. All authors have read and agreed to the published version of the manuscript.
Funding: This article is an outcome of the RECONECT project (Regenerating ECOsystens with Nature-
based solutions for hydro-meteorological risks eEduCTion). This project received funding from the
European Union’s Horizon 2020 research and Innovation Program under grant agreement No. 776866.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Acknowledgments: The authors wish to thank Riccardo Buelli, Benedetto Mortola and Francesco
Olivari for the support, the data provided and the useful discussion on landslide of Portofino’s
Promontory.
Conflicts of Interest: The authors declare no conflict of interest.
Land 2021, 10, 162 21 of 28

Appendix A
Summary of AHP analysis: pairwise comparison matrix computed by five participants, normalized principal eigenvector value and associated errors for
landslide conditioning factors.

Normalized
Factor 1 2 3 4 5 6 7 8 9 Principal Error
Eigenvector
Lithology 1 3 3/4 2/7 1/3 2 2/5 2 8/9 5 3/4 3 4/5 3 2/3 0.155 0.076
Aspect 1/4 1 1/4 3/8 2 2 1/4 2 3/8 2 1/7 3 0.090 0.040
Acclivity 3 4/7 3 3/4 1 2 5/9 4 1/3 7 7 6 1/8 4 1/8 0.311 0.107
Land use 2 3/4 2 3/5 2/5 1 2 3/5 3 4 1/2 4 1/2 4 1/2 0.197 0.093
Terraced area 2/5 1/2 1/4 3/8 1 4 2/7 3 1/4 4/9 5/9 0.064 0.033
Distance hydrographic
1/3 4/9 1/7 1/3 1/4 1 7/9 5/9 3/8 0.034 0.015
elements
Distance man-made cuts 1/6 3/7 1/7 2/9 1/3 1 2/7 1 5/8 3/8 0.031 0.015
Distance man-made
1/4 1/2 1/6 2/9 2 2/9 1 4/5 1 3/5 1 3/4 0.055 0.003
structures
Existing landslides 2/7 1/3 1/4 2/9 1 7/9 2 3/5 2 3/5 1 1/3 1 0.063 0.024

Appendix B
Summary of AHP analysis: pairwise comparison matrix computed by five participants, normalized principal eigenvector value and associated errors for
the classes within each landslide conditioning factor.

Normalized
Factor Classes 1 2 3 4 5 6 7 8 9 10 Principal Error
Eigenvector
Lithology
Alluvial deposits 1 1/5 2/7 1/8 1/8 0.031 0.016
Slope covers 5 1 2 1/6 1/7 1/6 0.093 0.040
Fills 3 3/8 1/2 1 1/7 1/6 0.061 0.022
Flysch 8 1/3 7 1/6 7 1/9 1 2 6/7 0.507 0.218
Conglomerate 7 3/4 6 1/5 6 1/3 1 0.308 0.140
Land 2021, 10, 162 22 of 28

Normalized
Factor Classes 1 2 3 4 5 6 7 8 9 10 Principal Error
Eigenvector
Aspect
N 1 1/3 1/5 1 1/7 2/7 2/9 1/6 1/8 2 5/9 0.318 0.012
NE 3 1 1/4 2 3/8 1 2/5 2/7 1/6 4 1/2 0.617 0.020
E 5 4 1/3 1 6 1/2 5 3 2/7 2 3/4 3/4 7 0.247 0.081
SE 7/8 3/7 1/7 1 1/3 2/7 2/7 1/4 1 8/9 0.355 0.014
S 3 3/8 1 1/5 2 3/4 1 3/8 1/3 1/5 3 2/7 0.628 0.021
SW 4 4/7 2 1/2 1/3 3 1/3 2 5/7 1 5/9 2/7 5 1/3 0.111 0.033
W 5 4/7 3 3/5 1/3 3 1/3 3 1 7/9 1 1/3 6 1/7 0.144 0.046
NW 7 3/4 6 1/7 1 1/3 4 4 5/6 3 2/3 3 1/9 1 8 1/6 0.285 0.098
Zenith 2/5 2/9 1/7 1/2 1/3 1/5 1/6 1/8 1 0.021 0.008
Acclivity
0–10% 1 1 1/4 1/4 1/7 1/5 1/5 0.033 0.010
11–20% 1 1 1/4 1/4 1/6 1/5 1/6 0.033 0.011
21–35% 4 1/3 4 1/8 1 1 1/4 2/7 2/7 0.090 0.032
36–50% 4 1/8 4 1 1 1/4 2/7 1/3 0.090 0.027
51–75% 6 4/7 6 4 4 1 2 2 1/3 0.331 0.102
76–100% 5 2/5 5 1/2 3 2/3 3 4/9 1/2 1 1 1/7 0.221 0.064
>100% 5 2/9 5 2/3 3 4/9 3 3/7 7/8 1 0.202 0.057

Appendix C

Normalized
Factor Classes 1 2 3 4 5 6 7 8 9 10 Principal Error
Eigenvector
Land Use
Urban fabric 1 5 3 5/8 5 2/5 4 8/9 4 1/2 1/5 3 1/4 4 4/7 0.149 0.042
Industrial/commercial/
1/5 1 1 1/7 1 1/3 2/9 2/3 1 1/7 4/9 1 1/7 0.038 0.009
transport units
Artificial
2/7 7/8 1 1 1/7 1/5 1 1/2 1 1/7 1/7 1/2 1 1/7 0.041 0.006
non-agricultural areas
Arable land 1/5 3/4 7/8 1 1/6 1 1/3 3/4 1/7 3/5 3/5 0.034 0.008
Land 2021, 10, 162 23 of 28

Normalized
Factor Classes 1 2 3 4 5 6 7 8 9 10 Principal Error
Eigenvector
Permanent crops 2 1/2 4 5/9 5 5 1/2 1 5 1/8 4 3/4 1/3 4 2/9 5 5/9 0.204 0.067
Pastures 1/5 1 3/7 2/3 3/4 1/5 1 7/8 1/7 2/5 2/3 0.034 0.011
Heterogeneous
2/9 1 7/8 1 1/3 1/5 1 1/7 1 1/7 1/2 1 0.038 0.006
agricultural areas
Forests 4 3/4 7 1/9 6 5/7 6 5/7 3 6 5/7 7 1/4 1 7 7 1/3 0.358 0.164
Shrubs ecc. 1/3 2 2/9 1 8/9 1 2/3 1/4 2 5/9 2 1/6 1/7 1 1 3/4 0.064 0.015
Open space
2/9 7/8 7/8 1 2/3 1/6 1 1/2 1 1/7 4/7 1 0.040 0.008
with little/no vegetation
Terraced areas
Area with terraces 1 1/3 0.261 -
Area without terraces 2 5/6 1 0.739 -
Hydrographic elements distance
Watercourses, d > 10m 1 6 1/3 2/3 6 6/7 0.394 0.077
Watercourses, d < 10m 1/6 1 1/6 2 0.081 0.002
Springs, d > 10 m 1 3/7 6 1/3 1 6 6/7 0.471 0.081
Springs, d < 10 m 1/7 1/2 1/7 1 0.054 0.012
Man-made cuts distance
Man-made cuts,
1 6 1/2 5 4/7 6 3/4 0.672 0.119
d>5m
Trails, d < 5 m 1/7 1 1 1/7 1 7/9 0.127 0.018
Main roads, d < 5 m 1/6 7/8 1 1 7/9 0.122 0.013
Minor roads, d < 5 m 1/7 5/9 5/9 1 0.79 0.014
Man-made structures distances
Man-made structures,
1 3 8/9 5 2/9 2 5/6 0.540 0.104
d > 10 m
Buildings, d < 10 m 1/4 1 2 5/6 3/5 0.160 0.038
Other manufacts,
1/5 1/3 1 1/3 0.077 0.019
d < 10 m
Retaining walls,
1/3 1 2/3 3 1 0.222 0.029
d < 10 m
Land 2021, 10, 162 24 of 28

Normalized
Factor Classes 1 2 3 4 5 6 7 8 9 10 Principal Error
Eigenvector
Existing landslides (IFFI Project)
Inactive/stabilized 1 1/2 2/9 1 1/2 1/7 1/6 0.044 0.015
Dormant 2 1/6 1 1/3 1 1/3 1/6 1/5 0.060 0.017
Active/reactivated/
4 4/7 3 1/9 1 4 3/8 1/5 1/4 0.133 0.053
suspended
Area affected by
widespread shallow 2/3 3/4 2/9 1 1/7 1/6 0.040 0.012
landslides
Assumed stable area,
6 2/3 6 1/7 5 1/3 6 2/3 1 2 0.423 0.171
d > 50 m
Assumed stable area,
5 7/9 5 1/7 4 2/7 6 4/9 1/2 1 0.300 0.123
d < 50 m
Land 2021, 10, 162 25 of 28

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