Landslide susceptibility assessment using the bivariate statistical analysis and the index of...

ORIGINAL ARTICLE

Landslide susceptibility assessment using the bivariate statisticalanalysis and the index of entropy in the Sibiciu Basin (Romania)

Mihaela Constantin • Martin Bednarik •

Marta C. Jurchescu • Marius Vlaicu

Received: 19 June 2009 / Accepted: 13 August 2010 / Published online: 5 September 2010

� Springer-Verlag 2010

Abstract The Sibiciu Basin is located in Romania

between the Buzau Mountains and the Buzau Subcarpa-

thians (Curvature Carpathians and Subcarpathians). The

geology of the basin consists of Paleogene flysch deposits

represented by an alternation of sandstones, marls, clays

and schists and Neogene deposits represented by marls,

clays and sands. The area is affected by different types of

landslides (shallow, medium-deep and deep-seated fail-

ures). In Romania, in the last decades, direct and indirect

methods have been applied for landslide susceptibility

assessment. The most utilized before 2000 were based on

qualitative approaches. This study evaluates the landslide

susceptibility in the Sibiciu Basin using a bivariate statis-

tical analysis and an index of entropy. A landslide inven-

tory map was prepared, and a susceptibility estimate was

assessed based on the following parameters which influ-

ence the landslide occurrence: slope angle, slope aspect,

curvature, lithology and land use. The landslide suscepti-

bility map was divided into five classes showing very low

to very high landslide susceptibility areas.

Keywords Landslide susceptibility �Bivariate statistical analysis � Index of entropy �Sibiciu Basin � Buzau Mountains � Romania

Introduction

Varnes (1984) defined landslides hazard as ‘‘the probability

of occurrence of a potentially damaging phenomenon

(landslide) within a given area and in a given period of time’’.

Landslide susceptibility is defined as the spatial probability

of landslide occurrence (Glade 2001; Sorriso-Valvo 2002;

Paudits and Bednarik 2002; Moreiras 2005; van Westen

2004; Guzzetti et al. 1999, 2006).

Different methods to prepare landslide susceptibility

and hazard maps exploiting statistical methods and GIS

tools were developed in the last years (Carrara et al.

1995; Glade 2001; Sorriso-Valvo 2002; Paudits and

Bednarik 2002; Moreiras 2005; van Westen 2004; Guz-

zetti et al. 2006). Direct and indirect methods have been

applied to prepare landslide susceptibility maps (Soeters

and Van Westen 1996; Carrara et al. 1995; van Westen

2004).

In Romania, before year 2000, direct and indirect

methods (represented by the qualitative analysis of land-

slide occurrence conditions) have been used by geomor-

phologists (Balteanu 1983; Balteanu et al. 1994; Cioaca

et al. 1993; Constantin et al. 2005; Constantin 2006a, b,

2008; Ielenicz et al. 1999) and were based especially on the

expert knowledge (Constantin 2008). This qualitative

method delineates landslide susceptibility classes using

different types of conventional signs and was used by many

Romanian researchers dealing with geomorphologic risk

evaluation over the period 1986–2000.

After 2000, the development of GIS applications

prompted also the usage of GIS tools in landslide suscep-

tibility assessment. The approaches were based mainly on

the analysis of landslide occurrence conditions. The the-

matic maps presenting lithology, slope aspect, slope angle,

etc., were classified, each class receiving a score according

M. Constantin (&) � M. C. Jurchescu � M. Vlaicu

Institute of Geography, Romanian Academy,

Str. D. Racovita 12, 023993 Bucharest 20, Romania

e-mail: [email protected]

M. Bednarik

Department of Engineering Geology, Comenius University,

Mlynska dolina G-127, 84215 Bratislava, Slovak Republic

123

Environ Earth Sci (2011) 63:397–406

DOI 10.1007/s12665-010-0724-y

to the landslide density. The landslide susceptibility map

was prepared using the summation of the scores (Const-

antin 2006a, b, 2008).

This paper presents a landslide susceptibility assess-

ment in the Sibiciu Basin using a bivariate statistical

analysis. Bivariate statistical analysis is based on the

comparison between a landslide inventory map as a

dependent variable and the single input parametric maps

(lithology/landslides, slope angle/landslides, etc.). The

approach used in the study area allows calculation of

weights for each input variable and is based on the

methodology proposed by Vlcko et al. (1980) where the

weight value for each parameter is expressed as an

entropy index.

The study area

The Sibiciu Basin (Fig. 1) is located between the Buzau

Mountains and the Buzau Subcarpathians (the Curvature

Carpathians and Subcarpathians). The basin has an area of

47.11 km2. The elevation is higher in the northern part of the

basin (Zboiu Peak, 1,114 m a. s. l.; Oii Peak, 1,037 m a. s. l)

and decrease towards the Buzau Valley in the south. The

drainage density has different values: in the Buzau Moun-

tains values are typical for medium size fragmented moun-

tains (5–6 km/km2) while in the Buzau Subcarpathians,

values are of 3–4 km/km2, typical for the hilly area. Slopes

with declivity of 12�–24� covers the higher area in the basin

(43.2%) followed by slopes with declivity of 24�–36� (27%).

Fig. 1 The location of the

Sibiciu Basin

398 Environ Earth Sci (2011) 63:397–406

123

The lithology is represented by Paleogene, Neogene and

Quaternary deposits. The Paleogene deposits out crop in

the Buzau Mountains in two different facies: the Colti

facies (Pg1?2 in age) and the Kliwa Sandstone Formation

(lf-ch in age). The Colti deposits are represented by

sandstones, clays and marls. The Kliwa Sandstone For-

mation is represented by sandstones interbedded by bitu-

minous shales and clays. These deposits cover a large area

of the basin (78%) and are responsible for the landslide

occurrence in the basin. The Neogene formations cover an

area of 21.4%, and consist of Helvetian, Badenian and

Sarmatian deposits (marls, clays and sands) occurring in

southeastern part of the basin. (the Buzau Subcarpathians).

The Helvetian deposits were mapped using the geological

map 1:200,000 (year 1968); nowadays, these deposits are

considered to be Burdigalian (bd).The Quaternary (qh1–

qh2) deposits (0.6%) are eluvial, slope and alluvial sedi-

ments represented by sands, clays and loess deposits.

Landslides are frequent on Paleogene and Neogene

deposits (especially on Sarmatian deposits represented by

clays and sands).

The land use is represented by forests (67.9%), pastures

(23.8%), orchards (6.3%) and a small percentage (2%) of

rivers, roads and settlements.

The main landslide triggering factors are rainfall and

snow-melting. The mean annual temperature is 9.6�C at

Patarlagele (10 km South West from the Colti village).

The largest amount of rainfall measured during one

month was 282.2 mm as recorded on July 1975, whereas

the largest amount of rainfall measured during 24 h was

177.8 mm as recorded on 02 July 1975. An examination of

the available rainfall data recorded during 1961–1980 in

the Curvature Carpathians and Subcarpathians revealed

that precipitation in excess of 200–300 mm can occur

during the summer months when the amount of rainfall can

be 2–5 times higher than the monthly average quantities

(Bogdan and Niculescu 1999). Figure 2 shows the maxi-

mum, minimum and mean monthly rainfall values mea-

sured at Patarlagele during the period of 1961–1995. This

information shows that rainfall values higher than 200 mm

were registered in May and July and were three times

higher than the monthly quantities. The freeze–thaw phe-

nomenon was observed to occur within a depth of about

55 cm from the ground surface during the months of

December through March according to the temperature

readings taken by several thermometers installed at dif-

ferent depths into the soil (Bogdan and Mihai 1977).

Among the mass movements (mainly rock falls and

landslides) affecting the basin, landslides have an impor-

tant role in the landscape modeling.

The main types of landslides occurring in the Sibiciu

Basin comprise shallow, medium-deep and deep-seated

landslides (Fig. 3). Based on field observation (Balteanu

1983), the landslides were classified according to the

depth and their volume. The shallow landslides have a

depth of 1.5–2 m and a volume of 10–104 m3, the

medium deep-seated have a depth of 2–10 m and a volume

of 104–105 m3 while the deep-seated have a depth of more

than 10 m and a volume higher than 105 m3 (Balteanu

1983; Ielenicz 1984).

Materials

The bivariate statistical analysis required the preparation of

landslide conditioning maps (lithology, slope angle, slope

aspect, curvature and land use) and the landslide distribu-

tion map (Fig. 4).

Based on field surveys a landslide inventory was pre-

pared. Aerial photos at 1:5,000 scale were also analyzed.

Maps of morphometric conditioning factors (slope angle,

slope aspect, curvature) were derived from the digital ele-

vation model. The geological information was obtained from

maps at 1:200,000 scale, whereas the land use map was

elaborated using aerial photos 1:5,000 scale (year 2005).

Thematic maps

Lithology

The geological information was obtained from the avail-

able map at 1:200,000 scale (maps at a larger scale are not

available for the basin). Six geological classes were iden-

tified: Paleocene–Eocene (Pg1?2)-sandstones, clays and

marls; Lattorfian–Chattian (lf-ch) sandstones, bituminous

shales and clays; Helvetian-marls; Badenian-sandstones

and marls; Sarmatian-clays and sands) and Quaternary

(qh1–qh2) loess, sands and clays. The distribution of the

lithological units is shown in Fig. 4a.

0

50

100

150

200

250

300

I II III IV V VI VII VIII IX X XI XII

month

mm

Mean

Maximum

Minimum

Fig. 2 The mean, maximum and minimum monthly precipitation at

Patarlagele (1961–1995)

Environ Earth Sci (2011) 63:397–406 399

123

The following morphometric parameters were assessed:

slope angle, slope aspect, curvature. The digital elevation

model was provided by the National Geodetic Fund in form

of a 10-m-density grid of elevation points. A digital ele-

vation model (DEM) with a resolution of 10 m 9 10 m

was produced in ArcGIS and was checked for elevation

errors.

Slope angle

The slope angle map derived from the DEM was reclassified in

seven angle classes for the analysis. The slope map was clas-

sified according to regional field observations of the frequency

of the slope mass movements (Balteanu 1983) (Fig. 4b).

Slope aspect

The slope aspect map was also obtained from the digital

elevation model and eight classes of 45� were considered in

our study (Fig. 4c).

Curvature

The curvature map was calculated as a second derivation of

the DEM. Three classes—concave, flat, convex—were

considered (Fig. 4d) for the analysis.

Land use

The map of land use was prepared using aerial photos

interpretation considering the following classes: pastures,

forests, orchards, rivers, roads and settlements (Fig. 4e).

Landslide inventory map

Spatial information about registered (mapped) landslides

represents the most important input factor in the process of

landslide susceptibility assessment.

The landslide inventory parametric map presents a

binary dependent (dichotomic) variable which is compared

with all input parametric maps in the process of bivariate

statistical analysis. The binary grid (raster) includes the

values 0 and 1 only (True/False), where a value of 1

indicates the presence of a landslide in a cell, and the value

0 denotes its absence (Bednarik 2007).

For bivariate analysis, the areas corresponding to the

main scarps were distinguished as a polygon shape. In the

study area we mapped 169 landslides (Fig. 4f).

Methods

Bivariate statistical analysis

To evaluate landslide susceptibility, we used the bivariate

analysis and the index of entropy (Brabb 1985; Carrara

et al. 1991; van Westen 2004; Bednarik 2007). A

10 m 9 10 m grid was utilized.

Fig. 3 The main types of landslides in the Sibiciu Basin: a shallow,

b medium-deep and c deep-seated landslides


123

Fig. 4 Thematic maps:

a Geology, b slope angle,

c slope aspect, d curvature,

e land use, f landslide inventory


123

The bivariate statistical analysis is based on the com-

parison, within a GIS environment, between the landslide

distribution and the selected parametric maps (lithology/

landslides, slope angle/landslides, etc.). This approach

allows calculation of the weight for each input variable.

Herein, the weighting process is based on the methodology

proposed by Vlcko et al. (1980). The weight value for each

parameter taken separately is expressed as an entropy

index.

Weighting process

The weight parameter was obtained from the defined level

of entropy representing the approximation to normal dis-

tribution of the probability. The entropy index indicates the

extent of disorder in the environment. It also expresses

which parameters in a natural environment are most rele-

vant for the development of mass movements. The calcu-

lated weights of individual parameters form the Wi value in

the landslide susceptibility assessment equation (Bednarik

et al. 2009).

The equations used to calculate the information coeffi-

cient Wj representing the weight value for the parameter as

a whole are:

pij ¼Asd

Atð1Þ

pij

� �¼ pijPsj

j¼1 pij

: ð2Þ

Hj and Hj max represent entropy values (Eqs. 3, 4).

Hj ¼ �Xsj

i¼1

ðpijÞ log2 pij

� �; j ¼ 1; . . .; n ð3Þ

Hjmax ¼ log2 sj; sj � number of classes ð4Þ

Ij is the information coefficient (Eq. 5) and Wj represents

the resultant weight value for the parameter as a whole

(Eq. 6).

Ij ¼Hj max � Hj

Hj max

; I ¼ 0; 1ð Þ; j ¼ 1; . . .; n ð5Þ

Wj ¼ Ijpij: ð6Þ

In Table 1, At represents the area of the category and Asd

is the area of landslides within the given category. Based

upon calculated probability density (pij), each input

parametric map was secondarily reclassified (column

recl_2) for final susceptibility map elaboration. Hj and

Hjmax represent entropy values, Ij is the information

coefficient and Wj represents the resultant weight value

for the parameter as a whole.

Landslide susceptibility map

The final landslide susceptibility map was prepared by

summation of weighted multiplications of the secondarily

reclassified parametric maps. The equation used for the

creation of the landslide susceptibility map has the fol-

lowing form:

y ¼ landuse recl2� Wj of land use� �

þ geol recl2

� Wj of geology� �

þ slope recl2� Wj of slope� �

þ aspect recl2� Wj of aspect� �

þ curvat recl2

� Wj of�

curvatureÞ ð7Þ

where y is value of landslide susceptibility in the final map;

/landuse_recl2/, value in a particular cell for the second-

arily reclassified parametric map of actual landuse; /

geol_recl2/, value in a particular cell for the secondarily

reclassified parametric map of lithological units; /slope_r-

ecl2/, value in a particular cell for the secondarily reclas-

sified parametric map of slope angles; /aspect_recl2/, value

in a particular cell for the secondarily reclassified para-

metric map of aspect; /curvat_recla2/, value in a particular

cell for the secondarily reclassified parametric map of

curvature.

The result of this summation is a continuous interval of

values from 0.0090 to 0.0957, and these represent the

various levels of the landslide susceptibility. Since these

intervals should be divided into three or five classes, here, a

natural breaks classification method was used to divide the

interval into five classes based on the Eq. 8 below:

Table 1 The result of weight calculation applied for geology

Parameter Class Atðkm2Þ Asdðkm2Þ Pij (Pij) Hj Hj max Ij Wj recl_2

Lithology 1 (Pg1?2) 6.9903 0.6989 0.1000 0.3704 2.1969 2.5850 0.1501 0.0068 6

2 (lf-ch) 29.7266 1.3963 0.0470 0.1740 4

3 (he) 4.5669 0.1463 0.0320 0.1187 2

4 (qh1–qh2) 0.2858 0.0000 0.0000 0.0000 0

5 (sm) 4.3496 0.2212 0.0509 0.1884 5

6 (bn) 1.1898 0.0477 0.0401 0.1485 3

Summation 47.1090 2.5104 0.2699 1.0000


123

SSDi...j ¼Xj

n¼i

A½n� �medianð Þ2 ð8Þ

where SSD is the sum of the squared differences; A, data

set (in ascending order).

Natural breaks method follows the Gauss normal dis-

tributions and it was used for classifying the results.

Results

The result of the bivariate analysis based on the total number

of mapped landslides shows that each class of the condi-

tioning factors is characterized by a certain landslide occu-

rence density. According to the pij values (affected area/total

area of each class), all thematic maps were reclassified.

Fig. 5 shows the (pij) values for each class of conditioning

factors. An analysis of the landslide distribution in relation to

the geological formation types based on the reclassification

process associated with the bivariate analysis indicates that

the Paleocene–Eocene facies of Colti exhibit the highest

score, followed by the Sarmatian alternation of clays and

sands (Sm), and the lf-ch deposits represented by Kliwa

facies (sandstones, schists and clays) (Table 1; Fig. 5a).

In terms of slope angle and slope aspect, most of the

existing landslides are distributed between the 12�–24� and

6�–12� slope classes (Fig. 5b), and between the east and

south facing slopes (Fig. 5c), respectively. The effect of

slope curvature on landslide occurrence appears to be

insignificant, as indicated by the almost equally distributed

scores in Fig. 5d.

With regard to land use categories, pastures and orch-

ards seem to have the highest impact on the landslide

distribution in the area (Fig. 5e).

Overall, the most important factors controlling the

landslide distribution derived from the weighting process

described by Eq. 7 are land use and geology.

The final landslide susceptibility map has been devel-

oped using the following equation:

y ¼ landuse recl2� 0:0073þ geol recl2� 0:0068

þ slope recl2� 0:0010þ aspect recl2� 0:0005

þ curvat recl2� 0:0001: ð9Þ

This map is shown in Fig. 6 and comprises the following

five landslide susceptibility classes:

1. very low (0.0090–0.0375),

2. low (0.0375–0.0569),

3. medium (0.0569–0.0709),

4. high (0.07089–0.0838),

5. very high (0.0838–0.0957).

The landslides falling under the very high susceptibility

class cover 5.4% of the study area, and are located in the N–

Fig. 5 Distribution of landslides within each conditioning factor according to the density of probability (pij): a Lithology versus landslides,

b slope angle versus landslides, c slope aspect versus landslides, d curvature versus landslides, e land use versus landslides


123

NW and S–SE corners of the Sibiciu Basin. The high landslide

susceptibility class covers 30.45% of the area throughout the

northwestern, central and southeastern parts of the basin. The

low and very low landslide susceptiblity classes cover only

small areas in the basin, whereas the moderate landslide sus-

ceptibility class covers 58.89% of the study area (Fig. 6).

The model performance (Clerici et al. 2009) was eval-

uated by comparing the landslide susceptibility map with

the landslide inventory map. The rate of success has been

estimated by comparing the landslide area corresponding to

the high and very high susceptibility classes with the total

area of the mapped landslides. A final success ratio of 56%

was obtained, which is not a very good outcome. A

potential reason for this outcome may be that no distinction

was made between shallow and deep-seated landslides in

the present analysis (i.e., they were considered together in

our study). Additionally, the utilized lithological map had a

scale of 1:200,000, thus, offering a rather scarce density of

information which was difficult to be compared with the

other data available at a high resolution.

In a further step, the validation of the model was per-

formed. The landslide database was split randomly into two

Fig. 6 The landslide

susceptibility map of the Sibiciu

Basin


123

datasets based on a 7:3 ratio. The first data set (i.e., 70%,

training set) was employed in the model construction,

whereas the other data set (i.e., 30%, validation set) was

used for the model testing (Clerici et al. 2006). New re-

classifications of each thematic layer were made based on

the not always new relationships established between the

conditioning factors and the 70% landslide distribution

dataset. The new scores were different only in terms of

aspect and curvature. The greatest score is still associated

with the east and the southeast facing slopes. Additionally,

the importance of concave and convex configured slopes is

switched.

Considering the weight of each conditioning factor

resulting from the 70% model construction, it can be seen

from Eq. 10 that the order of the parameters was not

changed.

y ¼ landuse recl2� 0:0062þ geol recl2� 0:0051

þ slope recl2� 0:0009þ aspect recl2� 0:0004

þ curvat recl2� 0:00001: ð10Þ

Only small differences between the two models can be

noticed in the percentage distribution of the susceptibility

classes within the Sibiciu Basin (a maximum difference of

0.6–0.7% in the case of moderate and high classes).

Discussions

The bivariate and multivariate statistical analyses are

mostly used for this kind of works with a high rate of

reliability. A bivariate statistical analysis has been adopted

for the landslide susceptibility assessment processing.

From among of the landslide susceptibility zonation

methods, the statistical analysis supported by GIS envi-

ronment seems to be suitable for medium and large-scale

(1:2,000–1:25,000) approaches (Clerici et al. 2009).

The bivariate statistical analysis and the index of

entropy utilized herein offer the possibility to compare the

landslide distribution map with each conditioning factor

and to assign weights.

The methodology was applied recently for several case

studies in Slovakia (Bednarik 2007; Bednarik et al. 2009)

and the rate of success was high. A number of 47 rock

slides and 268 earth slides were mapped out and were

compared with geology, DEM, aspect, land use and slope.

The findings have revealed that the most important

conditioning factor was geology, the rate of success being

70%. In our case study presented herein, 169 landslides

were taken into account and the result was that land use and

geology are the most important factors which explain better

the landslide occurrence. The rate of success was 58%

which was not a very good result comparing with the case

study from Slovakia where all input data were at similar

resolution (1:10,000). The main disadvantage for our case

study in Romania is the absence of data with similar res-

olution in digital form, which account for the unsatisfactory

rate of success mentioned above.

Conclusions

The Sibiciu Basin, located at the contact between the

Buzau Mountains (built on Paleogene flysch deposits rep-

resented mainly by alternations of sandstones and clays)

and the Buzau Subcarpathians (built by Neogene deposits

consisting of clays, marls and sands), has a large incidence

of landslides.

Based on the consideration that in general future land-

slide processes in the area will occur within old landslide

deposits or will be reactivations of the actual landslides, a

series of direct and indirect methods involving the land-

slide inventory map, landslide conditioning factors and

statistical tools available in a GIS environment were uti-

lized to develop a methodology for assessing the spatial

probability of landslides occurrence throughout the Sibiciu

Basin.

In order to assess the landslide susceptibility, we used

bivariate statistical analysis and the index of entropy with

definition of the weight of the parameters. The landslide

inventory map was compared with all thematic maps,

including lithology/landslides, slope angle/landslides, slope

aspect/landslides, curvature/landslides, land use/landslides).

According to the results of the bivariate statistical

analysis, the highest probability of landslide occurrence is

associated with Paleogene deposits (Pg1?2) comprising

the Colti facies (sandstones, clays and marls) and slope

angles of 12�–24�. Additionally, slope aspects falling in the

east, south and southwest quadrants show the highest

probability of landslide occurrence. Slope curvature is not a

major factor being characterized by an equal probability of

occurrence for each analyzed landslide susceptibility class.

A comparison of the landslide inventory map with the land

use map, revealed that the highest probability of landslide

occurrence is in areas covered by pastures and orchards.

The final map shows an area of very high landslide

susceptibility of 2.5 km2 (i.e., 5.4% of the total study area),

an area of high landslide susceptibility of 14.3 km2 (i.e.,

30.45% of the total study area), and an area of moderate

landslide susceptibility of 27.7 km2 (i.e., 58.9% of the total

study area). The low susceptibility class covers an area of

2.3 km2 (i.e., 4.86% of the total study area), whereas the

very low susceptibility class has an area of 0.19 km2 (i.e.,

0.4% of the total study area).

The model validation was conducted by developing a

new map using 70% of randomly selected landslides from

the first data set, and retaining the rest of 30% as the


123

validation set. By comparing the two models, only very

small differences between the areas of landslide suscepti-

bility classes were observed.

The analysis based on the entropy index characterizing

the level of chaos in the environment revealed that the most

unfavorable factors controlling the slope instability phe-

nomena throughout the study area are land use (pastures)

and geology (Paleocene–Eocene deposits).

The methodology applied herein can also be used in

landslide susceptibility assessment throughout other basins

with similar landslide occurence conditions. The assessment

based on the proposed methodology can be signficantly

improved if different types of landslides (e.g., shallow,

deep-seated) are considered and input data with similar

resolution are used. The landslide susceptibility maps

obtained using this method may represent a useful tool to the

authorities involved with territorial and land use planning.

Acknowledgments The present study was supported by the Min-

istry of Education and Research through the grant in aid PNII-

IDEI_367 (2007–2010) funded through The National University

Research Council of Romania (CNCSIS). The kind cooperation with

Department of Engineering Geology, Comenius University, Brati-

slava, is fully appreciated.

References

Balteanu D (1983) Experimentul de teren ın geomorfologie, Ed.

Academiei Romane, Bucuresti, pp 157 (in Romanian)

Balteanu D, Badea L, Dinu M, Cioaca A, Sandu M, Constantin M

(1994) Geomorphological hazards in the Buzau Subcarpathians,

Ed. Inst de Geografie, Bucuresti, pp 24 (in Romanian)

Bednarik M (2007) Landslide risk assessment as a base for land use

planning. PhD thesis, Faculty of Natural Sciences, Comenius

University in Bratislava, pp 130

Bednarik M, Magulova B, Matys M, Marschalko M (2009) Landslide

susceptibility assessment of the Kralovany–Liptovsky Mikulas

railway case study. In: Physics and chemistry of the earth, Parts

A/B/C. doi:10.1016/j.pce.2009.12.002 (article in press)

Bogdan O, Mihai E (1977) Ritmicitatea fenomenului inghet-dezghet

in Subcarpatii Buzaului, SCGGG-Geogr, XXIV, pp 31–44 (in

Romanian)

Bogdan O, Niculescu E (1999) Riscurile climatice din Romania, Sega

International, pp 280 (in Romanian)

Brabb EE (1985) Innovative approaches to landslide hazard and risk

mapping. In: Proceedings of the IVth international conference

and field workshop on landslides. Tokyo, pp 17–22

Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P

(1991) GIS techniques and statistical models in evaluating

landslide hazard. In: Earth surface processes and landforms.

16:427–445

Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) GIS-based

technology for mapping landslide hazard. In: Carrara A, Guzzetti

F (eds) Geographical information systems in assessing natural

hazards. Kluwer, Dordrecht, pp 35–175

Cioaca A, Balteanu D, Dinu M, Constantin M (1993) Studiul unor

cazuri de risc geomorfologic ın Carpatii de la Curbura, SCGGG-

Geogr, XL, pp 43–57 (in Romanian)

Clerici A, Perego S, Tellini C, Vescovi P (2006) A GIS-based

automated procedure for landslide susceptibility mapping by the

conditional analysis method: the Baganza valley case study

(Italian Northern Apennines). Environ Geol 50(7):941–961

Clerici A, Perego S, Tellini C, Vescovi P (2009) Landslide failure and

runout susceptibility in the upper T. Ceno valley (Northern

Apennines, Italy), Natural Hazards. doi:10.1007/sl1069-009-

9349-4

Constantin M (2006) The landslides distribution in the Niigata region

(Japan) and the Buzau Subcarpathians (Romania). Comparative

regional study. Civil Eng J 48(4):52–57 (in Japanese)

Constantin M (2006) Prognoza alunecarilor de teren. Abordari

Actuale, Ed.AGIR, Bucuresti, pp99 (in Romanian)

Constantin M (2008) The landslide susceptibility assessment in

Romania. A review of regional approaches. In: Proceedings of

the international conference on management of landslide hazard

in the Asia-Pacific Region, satellite symposium of the first world

landslide forum, 11–15 November 2008, Sendai, Japan, The

Japan Landslide Society, pp 510–518

Constantin M, Rotaru A, Nishimoto H, Yamakoshi T (2005)

Geomorphological hazards in Romania. Some examples from

the area situated at the contact between Buzau Carpathians and

Buzau Subcarpathians. J Jpn Soc Erosion Control Eng

58(1):59–62 (in Japanese)

Glade T (2001) Landslide hazard assessment and historical landslide

data-an inseparable couple. The use of historical data in natural

hazards assessment. Kluwer, Netherlands, pp 153–168

Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide

hazard evaluation; a review of current techniques and their

application in a multi-scale study, Central Italy. Geomorphology

37(1–2):181–216

Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006)

Estimating the quality of landslide susceptibility models. Geo-

morphology 81:166–184

Ielenicz M (1984) Muntii Ciucas-Buzau. Studiu geomorfologic., Ed.

Academiei, pp 146 (in Romanian)

Ielenicz M, Patru I, Mihai B (1999) Some geomorphologic types of

landslides in Romania, Transactions, Japanese Geomorpholog-

ical Union, 20–23, Tokyo. Romane, pp 287–299

Moreiras S (2005) Landslide susceptibility zonation in the Rio

Mendoza valley, Argentina. Geomorphology 66:345–357

Paudits P, Bednarik M (2002) Using GIS in evaluation of landslide

susceptibility in Handlovska Kotlina Basin. In: Rybar J,

Stemberk J, Wagner P (eds) Proceedings of the 1st European

conference on landslide. Swets and Zeitlinger, Lisse, Praha,

Czech Republic, 24–26th of June, 2002, pp 437–441. ISBN

90-5809-393 X

Soeters R, Van Westen CL (1996) Slope instability, recognition,

analysis, and zonation. In: Turner AK, Schuster RL (eds)

Landslides: investigation and mitigation. National Academy

Press, Washington, DC, pp 129–177

Sorriso-Valvo M (2002) Landslides: from inventory to risk. Land-

slides, Balkema, Rotterdam

van Westen CJ (2004) Geo-information tools for landslide risk

assessment: an overview of recent developments. In: Lacerda W,

Ehrlich M, Fontoura SAB, Sayao ASF (eds) Landslides:

evaluation and stabilization. Taylor and Francis Group, Balk-

ema, London, pp 39–57

Varnes DJ, Intern. Association of Engineering Geology Commission

on Landslides and Other Mass Movements on Slopes (1984)

Landslide hazard zonation: a review of principles and practice,

UNESCO, Paris, pp 63

Vlcko J, Wagner P, Rychlikova Z (1980) Evaluation of regional slope

stability. Mineralia Slovaca 12(3):275–283


123

http://dx.doi.org/10.1016/j.pce.2009.12.002

http://dx.doi.org/10.1007/sl1069-009-9349-4

http://dx.doi.org/10.1007/sl1069-009-9349-4

Landslide susceptibility assessment using the bivariate statistical analysis and the index of...

Documents

Transcript of Landslide susceptibility assessment using the bivariate statistical analysis and the index of...