International Journal of Geosciences, 2014, 5, 38-49
Published Online Januar y 2014 (http://www.scirp.org/journal/ijg)
http://dx.doi.org/10.4236/ijg.2014.51006
A New Statistic Approach towards Landslide Hazard
Risk Assessment
George Gaprindashvili1,2*, Jianping Guo3, Panisara Daorueang4, Tian Xin5, Pooyan Rahimy6
1Department of Geology, National Environmental Agency,
Ministry of Environment and Natural Resources Protection of Georgia, Tbilisi, Georgia
2Institute of Geo-Information Science and Earth Observation (ITC) of the University of Twente,
Enschede, The Netherlands
3Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing, China
4Department of Public Works and Town & Country Planning, Ministry of Interior, Bangkok, Thailand
5Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China
6School of Environmental Sciences, University of Guelph, Guelph, Canada
Received November 13, 2013; revised December 15, 2013; accepted January 3, 2014
Copyright © 2014 George Gaprindashvili et al. This is an open access article distributed under the Creative Commons Attribution
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ABSTRACT
To quantit ative ly asses s the la ndslide ha zard i n Khelva cha uri, Georgia, the statistic method of hazard index was
applied. A spatial database was construc ted in Geogr aphic Information System (GIS) inc l uding topographic data,
geologic maps, land-use, and active landslide events (extracted from the landslide inventory). After that, causal
factors of landslides (such as slope, aspect, lithology, geomorphology, land-use and soil depth) were produced t o
calculate the corresponding weights, and thereby we defined a relevant set of spatial criteria for the latter
landslide ha za rd assessment. On top of that, susceptibility assess ment was performed in order to classify the area
to low, moderate and high susceptible regions. Results showed that NW aspect, mountain geomorphology, pri-
vate land-use, laterite loam and clay, slope between 19 to 24 degrees, and soil depth between 10 - 20 cm were
found to have the largest contribution to high landslide susceptibility. The high success rate (72.35%) was ob-
tained using area under the curve from the landslide susceptibility map. M eanwhile, effect analysis was carried
out to assess the accuracy of the landslide susceptibility, indicating that the factor of slope played the most im-
portant role in determining the occurring probability of landslide although it did not deviate as much as other
factors. Finally, the vulnerability analyses were carried out by means of the Spatial Multi-Criteria Estimation
model, which in turn, led to the risk assessment. It turned out that not so much of the number of buildings (~
34.13%) was associated with high-risk zone and that governmental and private land-use almost accounted for
the same risk (39.9% and 40.9%, respectively).
KEYWORDS
Landslide; Weight; Susce pt ibility; Vulne r ability; Statistic
1. Introduction
Nowadays, quantitative landslide assessment is still in-
adequate due to too limited resources available for re-
search, such as historic records of landslides and detailed
socio-economic elements at risk. In particular, there are
no enough data available in order to construct a proba-
bilistic model of landslides at different magnitudes that
leads to a quantitative ris k asse ssment. Most co nvention-
al landslide studies ar e descriptive and qualitati ve; there-
fore, it is imperative for data-driven assessment in com-
bination with in-depth knowledge o f all the causal fa ctors
for landslide. The quantitative approach applied in this
study, is of great importance for the benefit of the gov-
ernment decision-makers, the urban planners and ulti-
*
Corresponding author.
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G. GAPRINDASHVILI ET AL.
39
mately the local communities.
The landslides frequently cause huge social and eco-
nomical disasters, posing threat to life and livelihood all
over the world. Many environmental factors related to
fields of geology, geomorphology, topography, and land-
use have the potential to induce landslides [1]. Tools for
handling and analyzing spatial data (i.e., GIS) facilitate
the application of quantitative techniques in landslide
hazard assessment and mapping. In terms of methods in
evaluating a landslide hazard, they can be categorized
into: geological, geotechnical, hydrological, geophysical
modeling, and statistical approach.
The work o f Lee and Jones [2] suggests that landslide
risk assessment methods should be classified as qualita-
tive, semi-quantitative and quantitative. The recent trend
towards the development of warning systems and land
utilization regulations aimed at minimizing the loss of
lives and damages to properties without investing in
long-term and costly projects of slope stabilization [3,4].
As a result, nowadays landslide hazard assessment, in-
cluding susceptibility and vulnerability mapping, increa-
singly becomes vital.
Recently, various studies have been carried out on
quantitative landslide susceptibility assessment along
with deterministic statistics [5-7] and even artificial in-
telligence [8-13]. Unfortunately, the above-mentioned
risk assessment methods are case-specific and require
many types of data on landslide occurrence and impact,
most of which, however, are not yet available in Georgia,
our study area of this paper.
To identify areas that are susceptible to future
landslides, it is very important to accurately detect past
landslides and to quantitatively formulate the relation
between the landslide occurrence and spatial occurrence
of environmental data. Therefore, this paper will present
a statistical method called “hazard index” to tackle the
issues of landslide susceptibility and risk analysis in
Khelvachauri, Georgia. This method is mainly based on
the landslides inventory map, generated by visual inter-
pretation of aerial photos, satellite images, and field sur-
veys. Six indicator maps contributing to the occurrence
of landslides will be combined as well. Susceptibility
assessment in this area was also carried out, and assessed
using the effect assessment. Finally, we constructed a
vulnerability map by comparing the produced landslide
susceptibilit y map with available data of elements at risk.
2. Study Area
The study area (Figure 1) is locat ed in Khel vachaur i, one
of the five municipalities in Adjara, an autonomous re-
public in the southwest Georgia, 8 km southeast from
one of the major cities o f Georgia - Ba tumi. Also, it cov-
ers an area of about 97.5 km2 with a population of ap-
pro ximate 38 ,000 , inc ludi ng the c it y of Khel vacha uri and
Figure 1. The study area of Khelvachauri, Georgia shown
within t he red boundary (Source: Google Earth).
Makhinjauri, as well as 30 villages. This area is bounded
by the B lack Sea to the west and by Turke y to t he south.
Within the study area, the most important and longest
river is Chorokhi River, which flows 26 km along the
region. Most of its parts pass through mountainous re-
gion, which is practically inaccessible for field explora-
tion. Agriculture dominates in the regional economy al-
though industry has been also d eveloped. There are three
tea factories, stagnant materials plant and constructing
blocks workshop. However, agriculture (especially in tea
and citrus production) as well as husbandry takes a lead-
ing part.
Landslides occur almost in all landscape - ge omor-
phologic zones, which makes that there is a wide diffe-
rentiation in the failure types and mecha nisms a nd in the
size-frequency distribution. Negative impact of lands-
lides are in the form of destruction of buildings, agricul-
tural lands, roads and other infrastructure and also to
considerable effect on population in the form of loss of
human li fe and a significant number of eco-migrants .
The relief, geology, geomorphology of the territory of
Khelvachauri creates favorable conditions for the devel-
opment of active geological processes, such as landslides,
mudflows/d ebrisflows and rockfalls. Landslide processes
affecting the social and economic development of the
country are widespread in this region.
3. Data Description
In order to assess the landslide hazard triggered by rain-
fall i n Khel vachauri , Georgi a, we fir st have to obta in the
lands lide i nvento ry map, which was limited to the per iod
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40
of 2000 to 2006, with the original landslide data from
Ministry of Environmental Protection and Natural Re-
sources of Georgia. The inventory recorded 45 landslides,
almost belonging to the active states. Typically, land-
slides were visually interpreted by comparing ortho-rec-
tified aerial photographs taken before and after the oc-
curring of landslides, in conjunction with high-resolution
Google-Eart h i mages a nd inte nsive fiel d work ( Figure 2).
Finally we derived the landslide inventory map, includ-
ing all types of landslides and their corresponding infor-
mation, eg. occurring time and the degree of damage.
The causal factors and elements at risk are prerequi-
sites for the latter risk and vulnerability assessment,
which is describ e d in detail in the following sections.
3.1. Causal Factors
The causes of landslides have b een classified into rainfall,
earthquakes, erosion weathering, groundwater level, and
human activities [14]. Even though there are always
more than one single cause for the occurrence of
landslides, according to the records of Ministry of Envi-
ronment and Natural Resources Protection of Georgia,
the active landslides in this study area, nevertheless, are
mainly induced by the rainfall.
In theory, the more possible causal factors considered,
the more accurate landslide susceptibility and risk as-
sessment. To this end, we tried our best to collect such
data as topography, geotechnical and general soil mea-
surements, lithology, geomorphology, and satellite im-
ages. Nonetheless, the factor of rainfall, commonly
thought as a main triggering factor of landslide, was ex-
cluded primarily due to unavailability of enough weather
stations. The above-mentioned factors used here were
digitized based on the historical geological maps at the
scale of 1:10,000. Factors derived from topography, such
as slope gradient and slope aspect, were calculated from
the topographic data, generally considered as the most
influential factors. Furthermore, geomorphologic data
Figure 2. Landslide in Ortabatumi (Khelvachauri Munici-
pality).
were collected from historical map and the land-use data
were derived from parcel data of the cadastral database.
Soil depth data were measured via field survey investiga-
tion prior to landsl ide occurring.
3.2. Elements at Risk
Identifyi ng the elements at risk of landslide and vulnera-
bility assessment need the exact spatial distribution of
buildings in the study ar ea, as well a s the socio -economic
information like the number of stories, possession of
properties, economic values etc. All of the data consi-
dered as elements at risk were basically extracted from
cadastral data and participatory GIS procedures.
4. Methodology
4.1. Workflow
As shown in F ig ure 3, the workflow for the assessment
of landslide can be roughly described as follows:
The primary input data were lithology, geomorphology,
land use, soil depth and topographic data. Other informa-
tion (secondary data) was derived from the different in-
put data (e.g., slope and aspect from topographic). The
collectively called causal factors (primary and secondary
data) were used as inputs into the statistic model. This
was separately calculated to give the respective weight
and was finally analyzed for the susceptibility assessment.
Finally, the physical vulnerability was assessed by the
Spatial Multi-Criteria Estimation (SMCE) method [15]
and was computed through crossing the number of
building and land use information. It should be noted that
result s fro m the SMCE d id facilitate t he deter mination of
physical vul ner a bility in this study area.
Figure 3. Flowchart of susceptibility mapping and vulnera-
bility a ssessment of landsli de .
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4.2. Hazard Index Method
Due to the lack of enough landslide history and geotech-
nical data, the quantitative, deterministic or probabilistic
models were excluded from the study. As an alternative,
bivariate statistical vulnerability assessment of landslide
(a semi-quantitative approach) was applied here, just
based on the active landslide events. The method expli-
citly considered a number of factors influencing the sta-
bility of slope [1 6] , including the follo wing 6 p ara meters:
lithology, geomorphology, landuse, soil depth, slope and
aspect.
In order to assess the vulnerab ility of landslide hazard,
landslide susceptibility map was generated using a basic
statistical method, called hazard index, which was for-
mulated as:
()
()()
( )
Area SiArea Si
Densclas
lnln Area NiArea Ni
Densmap
i
W

== 



∑
∑
(1)
where Wi represented weight, and subscript i indicated
one of the 6 parameters: slope, aspect, lithology, geo-
morphology, land use and soil depth. Densclas was the
landslide density belonging to the corresponding para-
meter class. Densmap represented the landslide density
within the entire map. “Area (Si) “indicated area, which
contained landslides, for a given parameter class. Area
(Ni) referred to the total area for a given parameter class.
Noted that all the processes were performed in Integrated
Land and W ate r Info r mation Syst em (I LWI S), which i s a
GIS and remote sensing software developed by The Fa-
culty of Geo-Information Science and Earth Observation
of Twente Universi ty, Netherlands.
As illustrated in Figure 4, the slope was first derived
from Digital Elevation Model (DEM), and then was
weighted according to Equation (1 ). The other five causal
factor parameters can be assigned the corresponding
weight in that vein. Specifically, the method was based
on map by crossing landslide map at the active state with
one of the 6-parameter maps. The map crossing results
were shown in a cross table, which was utilized to calcu-
late the density of landslides for a given parameter class.
A standardization of these density values was obtained
by relating them to the overall landslide density in the
entire area. Here, the landslide density in the entire map
divided the landslide density per class. The natural loga-
rithm was taken, so it follows that when the landslide
density was lower than normal, you will get negative
weights, and positive when it was highe r than normal.
By mathematically adding up the weights of the 6 fac-
tor maps, a susceptibility map can be created. After that,
the values were classified into three classes: low, mod-
erate and high susceptibilities. A cross validation tech-
nique called “success rate” was performed to evaluate the
performance of the model. The pixels of the sum of the 6
factor maps were arranged from high to low values based
on the frequency information of the histogram and were
categorized into 100 classes. Subsequently, a joint fre-
quency was calculated with the overlaid active landslide
map and summed 6 factor maps and was presented as a
cumulative percentage of landslides and the percentage
of map’s area. The area under the curve was calculated as
well to assess the accurac y of the map.
Effect analysis can further show how success rate
changes when the input factors are changed and quanti-
fies the uncertainty of each factor [17]. Effect analysis
was done by excluding a single factor from the summa-
tion of the other factors in such a way that for example
the aspect was excluded and the weights of geomorphol-
ogy, lithology, land-use, slope, and soil depth were
summed up. This was repeatedly done for all the causal
factors.
5. Results
5.1. Causal Factors Mapping
Terrain parameters, i.e. slope and aspect were always
being thought as good indicators of the spatial criteria
required in SMCE-based landslide susceptibility assess-
ment. They were primarily derived from DEM data.
From the very beginning, we tried to derive terrain pa-
rameters from two different DEM source, i.e. ASTER
and topographic map. By comparing the contour line
from ASTER DEM and that of the 1:50,000 topographic
map, large errors were found (maximum 30 meters shift,
not graphically shown here) between them. As a result,
we selected DEM from topographic map as the data
source for the aspect and slope factors, and cautions
should be taken in the potential applications of ASTER
DEM in the assessment of landslides hazard. On top of
slope and aspect maps, other factors such as lithology,
geomorphology, soil depth and land use were shown in
Figure 5.
5.2. Active Landslide Extraction
Thirty-eight active landslides were extracted and taken as
the dependent variable in the model thus the susceptibil-
ity assessment was performed based exactly on this type.
Also, as indicated in Figure 6, the active landslides
spread sporadically everywhere in the study area, which
suggested that the situation in this area is very severe.
Given the multi-land use types and topographic factors in
this area, the assessments of landslide were getting more
complicated .
5.3. Weight Assignment
To identify the most influential causal parameters on
active landslides in the study area and quantify their cor-
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Figure 4 . The det a iled methodology employe d in landslide assessment.
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Figure 5 . Causal factor maps for slope (a), soil depth (b), aspect(c), land use (d), lithology (e), and geomorphology (f), respec-
tively.
Figure 6 . A ct ive landslide events extracted from the land- slide inventory over the study area.
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responding contributions, we calculated the weight of the
six parameters described in Section of Methodology us-
ing Equation (1). Various causal parameters had quite
different influences on the landslide occurrence, either
favorable or unfavorable [18]. The weight results for the
six causal parameters were not shown here. B y adding up
the weight of causal factors such as slope, soil depth,
geomorphology, aspect, land use and lithology, we got
the overall weight map, as shown in Figur e 7.
From the calculated weights in Table 1, the most im-
portant influe ntial s ubtypes o f causal factors related with
landslide were recognized. As for the aspect factor, NW
had the most important relation with landslides. The
mountain class of geomorphology was more prone to
landslide. Similarly, private land use and laterite loam
and clay represented the highest susceptibility for
landslide. The slope between 19 - 24 degrees and soil
depth between 10 - 20 cm were most associated with
landslide.
5.4. Susceptibility Assessment
Based on the weights assignment, we carried out the
susceptibility assessment. The final weights of the re-
sulting map ranged from −18.9 to 2.2. Although the
weight map (Figure 7) showed good indication of the
quantitative landslide hazard in the study area, too wide
range might make it difficult to utilize by decision mak-
ers for development planning. Therefore, the hazard map
was grouped into three simplified categories based on the
histogram of the final weight map (Figure 7): high,
moderate and low (Figure 8). Low hazard corresponded
to the range o f (−18.9, −4), the moderate to (−4, 1.1) and
the high one to (1.1, 2.2).
The landslide susceptibility map gave the spatial dis-
tribution of the relative susceptibility values for the
whole area. Figure 8 indicated that the moderate and
high susceptible zones had a more disperse pattern,
compared with the low susceptibility zone. Based on the
susceptibilit y in Figure 8, we got the statistics of area or
percentage of Landslide Susceptibility Classes, which
was given in Table 2. Results showed that the area of
27.7 km2 (28.4%) located in the high hazard zone, a more
considerable area (53.2% of the total area) was assigned
to moderate landslide susceptibility zone.
5.5. Assess ment an d Effec t Analy sis o f Model
The success rate curve [19] is of importance to the veri-
fication of susceptibility map of landslides, which was
performed by comparing the known landslide location
with the landslide susceptibility map. As such, the suc-
cess rate had been applied in many previous studies
[20,21] to assess landslide prediction model perfor-
mance.
In this paper, by assuming that the landslides were
Figure 7 . The overall weig ht produced by a v eraging out the weights of six c ausal f ac tors.
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Figure 8 . The sus cept ibi lit y ma p in our study area, which was cal culated from the haza r d index method appli ed in this s tudy.
Table 1. Most infl uential type or value range versus ea ch causal factor.
Causal factor Aspect Geomorphology L and-use Lithology Slope (degree) Soil depth (c m)
Most influential subtypes NW mountain private use laterite loam and clay 19 - 24 10 - 20
Table 2. Area and percentage of landslide susceptibility
classes.
Classification Area (km2) %
low hazard 18.0 18.4
mod er ate hazard 51.8 53.2
high hazard 27.7 28.4
Total area 97.5 100
linked to the causal factors (geomorphology, slope, as-
pect, soil depth, land-use, and lithology) and that the ex-
cessive rainfall serves as the trigger of the event, success
rate allowed an estimate of a good fit of the model
through statistical computations. As shown in Figure 9 (a),
the obtained success rate curve was very steep in the
former part, ind icative of great p redictive cap ability. Par-
ticularly, roughly 82% of the pixels predicted a 100%
landslide (indicated by red B in Figure 9(a)), whereas
more or less 50% of the pixels with the highest weight
value in the map showed 80% landslides (indicated by
blue A in Figure 9(a ) ).
Also, the area under the curve (AUC) was calculated
to quantify the validity of the model [22,23]. Total area
approaching 100 percent signifies perfect prediction
while an area under 50 percent represented an invalid
prediction. In this case, the area under the curve of the
overall success rate curve was 72.35%, which implied
that the model was valid.
In this study, the effect analyses were conducted by
exclusion of each factor in turn from the summation of
the weight factors. Related success rates were drawn and
the effect of each factor was evaluated using area under
the curve calculation, which was given in F ig ure 9(b).
Furthermore, there were no significant deviations of
success rate curves by excluding any causal factor from
the overall curve. Meanwhile, it can also be deduced
from Figure 9(b) that the most important factor on
landslide analysis, was the slope with AUC = 70.22%,
then t he important factor is follo wed by soil dep th (AUC
= 71.35%), lithology (AUC = 71.61%), land-use
(71.99%), geomorphology (AUC = 72.73%), and the
aspect (AUC = 72.80%).
5.6. Vulnerability Assessment
Vulnerabilit y should be considered in the physical, social,
environmental dimensions. However, due to the limited
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Figure 9 . Ca lculat e d o ver al l s uc ce ss r a te ba sed o n si x c aus al
factors (a), superimposed by the effect analysis results (b),
which was performed by exclusion of each factor every
time.
availability of da ta related to pop ulation and social p rop-
erties, the physical vulnerability assessment has only
been performed. The physical vulnerability was assessed
based on elements at risk of building and land-use.
The assessment results were given in Table 3. Within
this study area, a total number of 9909 buildings were
included. Amongst them, 3382 buildings (34.13%)
represented high susceptibility class, followed by 4584
buildings as moderate susceptible.
According to Table 4, within in study area, main land
use types were categorized as either private or public
owned by government. As for low susceptibility class
with an area of 17.9 km2, the governmental land use ac-
counts for about 26% and private about 23.3%. In mod-
erate susceptibility class, with an area of 51.9 km2, the
governmental land use accounts for about 26.6%, and
private about 25.6%. For the high susceptibility class
with an area of 27.6 km2, the governmental and private
Table 3. Physical vulnerability results (number of build-
ings).
Susceptibil ity classificatio ns N umber of buildings Percentage
High susceptibility 3382 34.13
Moderate susceptibility 4584 46.26
Low susceptibility 1943 19.61
Total 9909 100
Table 4. Physical vulnerability results (land use).
Hazard * Landuse Area (km2) %
Low hazard * government 4.7 26.0
Low hazard * private 4.2 23.3
Total Low susc eptibility class area 17.9
Moderate hazard * government 13.9 26.6
Moderate hazard * private 13.3 25.6
Total Moderate susceptibility class area 51.9
High hazard * government 1 1.0 39. 9
High hazard * private 11.3 40.9
Total High suscept ib il ity class area 27.7
Tot al a r e a of Khel v achauri 97.5
land use accounts for 39.9% and 40. 9 %, respectively.
6. Discussion and Conclusions
The results showed that despite the operational and con-
ceptual limitations, landslide hazard assessment should
be a suitable, cost-effective aid to land-use planning and
hazard reduction.
By calculating the respective weight for six different
causal factors, it was recognized the area with NW aspect,
mou nt ain geomorphology, private land-use, laterite loa m
and clay, 19 - 24 degree for slope and soil depth between
10 and 20 cm were among the most susceptible areas for
landslide occurrence. Primarily due to the lack of reliable
and high-resolution rainfall fields, few statistical models
have included rainfall variables as explanatory variables.
This was also the reason that this research did not take
into account the rainfall as an approach for the landslide
hazard and risk assessment. Moreover, the analysis was
done in terms of physical vulnerability (by overlaying the
number of buildings, including land-use data, in the dif-
ferent hazardous areas).
Landslide susceptibility maps are of great importance
to planners and engineers for choosing suitable locations
to implement eco-social developments. In this study, we
found that about 28.4% of the area was prone to high
landslide ris k.
The final landslide susceptibility map, with the com-
bination of all the weights, yielded a satisfactory predic-
tion of the landslide with a success rate of 82%. The role
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G. GAPRINDASHVILI ET AL.
48
of geomorphology, soil depth, lithology, and slope served
as very important factors for the landslide processes. The
models were proven valid through the calculation of the
area under the curve, i.e., all success rate curves occupy
more than 72.35% of the total area. Effect analysis
showed that slope played the most important role in the
landslide analysis since slope bore the greatest weight.
Therefore when all factor weights were added except for
the weight of the slope, the resultant weight greatly di-
minished which in turn reduced the percentages of
landslide.
Due to the lack of social, economic, environmental
and physical vulnerability data, only building and land
use vulnerability assessments were carried out using
SMCE model in our study. We found that 34.13% of the
numbers of buildings were represented as high suscepti-
bility class. W e esti mated as well that i n hig h susceptib il-
ity class there was 39.9% from the governmental land
use and 40.9% from the private.
The method presented here has a series of drawbacks,
which should be taken into account. For instance, the
landslide hazard map was only calculated from the oc-
currence of active landslides. Other landslide activities
should also be included for future research. Ho wever, the
use of landslide hazar d index statistics in K helvachauri is
useful for ranking them in order of importance for
landslide risk reduction measures. The method allows
evaluating which causal factor is responsible for high
susceptible and vulnerable of landslides. I t sho uld also b e
noted that the re sulting landslid e susceptibili ty value and
the vulnerability were not static [15]. The landslide sus-
ceptibility map and vulnerability value should therefore
be updated regularly since these indicators had temporal
variabilit y. For further study t o improve the v ulnerability
assessment results, it is highly recommended that other
factors such as river distance, number of population per
household, climate data, history of landslide event, once
become available, should be taken into account. This
would make landslide data more reliable. This would
make local (provincial and municipal) authorities accor-
dingly produce logical la ndslide mitigation program.
Ackno wledgements
This work was car ried out under the auspices of the Min-
istry of Science and Technology of China (Grant number:
2013CB733404). The authors wo uld like to thank Dr. C.J.
Cees va n Westen and M s. Drs. N. C. King ma wit h Inter-
national Institute for Geo-Information Science and Earth
Observation (ITC), University of Twente, the Nether-
lands for their kind suggestions and comments on the
validation analysis. The authors would like to thank Na-
tional Environmental Agency of Ministry of Environ-
ment and Natural Resources Protection of Georgia for
providing various datasets used in this s tudy.
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