GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods...

ORI GIN AL PA PER

GIS-multicriteria decision analysis for landslidesusceptibility mapping: comparing three methodsfor the Urmia lake basin, Iran

Bakhtiar Feizizadeh • Thomas Blaschke

Received: 4 February 2012 / Accepted: 18 October 2012 / Published online: 27 October 2012� Springer Science+Business Media Dordrecht 2012

Abstract The GIS-multicriteria decision analysis (GIS-MCDA) technique is increasingly

used for landslide hazard mapping and zonation. It enables the integration of different data

layers with different levels of uncertainty. In this study, three different GIS-MCDA

methods were applied to landslide susceptibility mapping for the Urmia lake basin in

northwest Iran. Nine landslide causal factors were used, whereby parameters were

extracted from an associated spatial database. These factors were evaluated, and then, the

respective factor weight and class weight were assigned to each of the associated factors.

The landslide susceptibility maps were produced based on weighted overly techniques

including analytic hierarchy process (AHP), weighted linear combination (WLC) and

ordered weighted average (OWA). An existing inventory of known landslides within the

case study area was compared with the resulting susceptibility maps. Respectively,

Dempster-Shafer Theory was used to carry out uncertainty analysis of GIS-MCDA results.

Result of research indicated the AHP performed best in the landslide susceptibility map-

ping closely followed by the OWA method while the WLC method delivered significantly

poorer results. The resulting figures are generally very high for this area, but it could be

proved that the choice of method significantly influences the results.

Keywords Landslide susceptibility � Multicriteria evaluation � GIS-multicriteria

decision analysis � Uncertainty analysis � Urmia lake basin � Iran

1 Introduction

Disaster management analysis is one of the important application domains of GIS-based

multicriteria decision analysis (GIS-MCDA). GIS-MCDA provides powerful techniques

B. Feizizadeh (&)Department of Physical Geography, Centre for Remote Sensing and GIS,University of Tabriz, Tabriz, Irane-mail: [email protected]; [email protected]

B. Feizizadeh � T. BlaschkeDepartment of Geoinformatics (Z_GIS), University of Salzburg, Salzburg, Austriae-mail: [email protected]

123

Nat Hazards (2013) 65:2105–2128DOI 10.1007/s11069-012-0463-3

for the analysis and prediction of hazards. Landslides are one of the destructive natural

phenomena that frequently lead to serious problems in rolling hillsides, resulting in loss of

human life and property, as well as causing severe damage to natural resources (Inta-

rawichian and Dasananda 2010). Landslides and slope instabilities are major hazards for

human activities often causing economic losses, property damages and high maintenance

costs, as well as injuries or fatalities (Das et al. 2010). These damages can be mitigated if

the cause and effect relationships of the events are known (Intarawichian and Dasananda

2010). Landslide susceptibility mapping (LSM) is a solution to understanding and pre-

dicting future hazards in order to mitigate their consequences. It is one of the study fields

portraying the spatial distribution of future slope-failure susceptibility (Lei and Jing-feng

2006). The LSM process is based on statistical or deterministic methods (Carrara et al.

1991; van Westen et al. 2008; Cervi et al. 2010) and could be the basis for decision-making

to help citizens, planners and engineers to reduce the losses caused by current and future

landslides by means of prevention, mitigation and avoidance (Feizizadeh and Blaschke

2011; Feizizadeh et al. 2012b). They provide important information for predicting land-

slides hazards which include an indication of the time scale within which particular

landslides are likely to occur (Atkinson and Massari 2011).

In creating a susceptibility map, the direct mapping method involves identifying regions

susceptible to slope failure, by comparing detailed geological and geomorphological

properties with those of landslide sites. The indirect mapping method integrates many

factors and weighs the importance of different variables using subjective decision-making

rules, based on the experience of the geoscientists involved (Lei and Jing-feng 2006). GIS-

MCDA provides a rich collection of techniques and procedures for structuring decision

problems and designing, evaluating and prioritizing alternative decisions. At the most

rudimentary level, GIS-MCDA can be thought of ‘‘as a process that transforms and

combines geographical data and value judgments (the decision-maker’s preferences) to

obtain information for decision-making. It is in the context of the synergetic capabilities of

GIS and MCDA that one can see the benefit for advancing theoretical and applied research

on GIS-MCDA’’ (Malczewski 2006a, p. 703). The chief rationale behind integrating GIS

and MCDA is that these two distinct areas of research can complement each other. While

GIS is commonly recognized as a powerful and integrated tool with unique capabilities for

storing, manipulating, analysing and visualizing spatial data for decision-making, MCDA

provides a rich collection of procedures and algorithms for structuring decision problems,

designing, evaluating and prioritizing alternative decisions. It is in the context of the

synergetic capabilities of GIS and MCDA that one can observe the benefits for advancing

theoretical and applied research on the integration of GIS and MCDA (Malczewski 1999,

2006a; Boroushaki and Malczewski 2010).

Although the GIS-MCDA approaches have traditionally focused on the MCDA algo-

rithms for individual decision-making, significant efforts have recently been made to

integrate MCDA with GIS in a group decision-making setting (Malczewski 1996;

Jankowski et al. 1997; Nyerges et al. 1997; Bennett et al. 1999; Feick and Hall 1999;

Jankowski and Nyerges 2001a, b; Kyem 2004). In a survey of GIS-MCDA systems for

collaborative decision-making, Malczewski (2006b) noted that the voting methods

(social choice functions) are the most popular approach for generating a group solution in a

GIS-based multicriteria group decision-making environment. Central to GIS-MCDA is the

concept of decision rules or evaluation algorithms. A decision (or combination) rule can be

defined as a procedure that enables the decision-maker to order and select one or more of

the alternatives from a set of available alternatives (see Starr and Zeleny 1977; Malczewski

1999). Over the last two decades, there have been a number of multicriteria decision rules

2106 Nat Hazards (2013) 65:2105–2128

123

implemented in the GIS environment including the weighted linear combination (WLC) or

weighted summation/Boolean overlay methods (e.g. Carver 1991), the analytic hierarchy

process (AHP) (e.g. Banai 1993), ordered weighted average (OWA) (Yager 1988), the

ideal/reference point methods (e.g. Eastman 1997) and the outranking methods (e.g. Joerin

et al. 2001). According to a comprehensive survey of the GIS-MCDA literature (see

Malczewski 2006a), the Boolean overlay operators and the weighted linear combination

procedures account for almost 40 % of all GIS-based multicriteria analysis applications

(e.g. Eastman 1997; Malczewski and Rinner 2005; Chen et al. 2009). These approaches

can be generalized within the framework of the OWA (Jiang and Eastman 2000;

Makropoulos et al. 2003; Malczewski et al. 2003; Malczewski and Rinner 2005; Borou-

shaki and Malczewski 2008).

GIS-based MCDA methods are increasingly being used in LSM (Feizizadeh and

Blaschke 2012b). The attempts of LSM have used GIS-based MCDA approaches including

the Boolean overlay and the WLC methods (Malczewski 1999). ‘‘The Boolean overlay

approach uses non-compensatory aggregation operators such as the intersection (AND)

where every criterion is met and the union (OR) where a single criterion is met. The WLC

approach uses compensatory aggregation rules where decision set includes the overall

value of the alternatives and where favourable criteria can outweigh unfavourable criteria.

The WLC procedure allows a full trade-off among criteria (i.e. high criteria weights can

compensate for low criteria scores) and offers much more flexibility than the Boolean

overlay approach’’ (Gorsevski et al. 2012, p. 288). This paper builds on some earlier results

from a study regarding landslide susceptibility assessment, applying the GIS-MCDA

procedure for the assessment of landslide hazards, which are common in the Urmia lake

basin, Iran. The main objectives of this research are to apply the GIS-MCDA approach for

the LSM of the Urmia lake basin and to compare three different methods to a spatial

database of known landslides in order to identify which method is more beneficial.

2 Study area and dataset

The Urmia lake basin is located in the northern-west of Iran (See Fig. 1). This area with 35

cities and 1,018 villages totalling in 3.2 million inhabitants is important in terms of

housing, industrial and agricultural activities for the East Azerbaijan province (Iran Census

Centre 2007; Feizizadeh et al. 2012c). In Urmia lake basin, the elevation increases from

1,260 m at Urmia Lake to 3,710 m above sea level in the Sahand Mountains. The climate

of this area is semi-arid and the annual precipitation amounts to approximately 300 mm.

The area’s geology is responsible for volcanic hazards, earthquakes and landslides. This

setting makes the slopes of the area potentially vulnerable to landslides and mass move-

ments (See Fig. 2 as example of recent landslide event in the study area). Mass movement

and landslides are common in the Urmia lake basin (Feizizadeh and Blaschke 2011). The

landslide inventory database of Urmia lake basin indicated 132 landslide events were

recorded by GPS in field survey (MNR 2010). Unstable slopes combined with the geo-

logical tectonic settings make this area highly susceptible for landslide hazards. In par-

ticular, the unstable slopes in the northern part of the Tabriz city (the largest city in the

northern-west of Iran with about 2 million inhabitants) have a high potential for mass

movements and landslides.

In order to produce a landslide susceptibility map of this area, we used topographic,

geological, climatic and socioeconomic parameters in our research. Major datasets used in

this study include:

Nat Hazards (2013) 65:2105–2128 2107

123

• Lithology and fault maps derived from geological maps 1:100,000.

• Road and drainage maps extracted from a topographical map of the area in a scale of

1:25,000.

• Digital topographical maps in a scale of 1:25,000 were used to create DEM and obtain

slope and aspect respectively.

• Land use/cover maps were derived from Landsat ETM ? satellite images with spatial

resolution 30 m based on image processing techniques (MANR 2008).

• A 30-year meteorological data were used to create precipitation map.

Fig. 1 Location of the case study area within Iran and the Persian Gulf region (left) and north-western Iran(right)

Fig. 2 Landslide event in study area

2108 Nat Hazards (2013) 65:2105–2128

123

• The landslides inventory database for the Urmia lake basin including 132 landslide

events was recorded by GPS in field survey (MNR 2010).

In the preparation phase, all necessary geometric thematic editing was done on the

original datasets and a topology was created. In the next step, all vector layers were

converted into raster format with 20 m resolution, and the spatial datasets were processed

in Arc GIS. Slope and aspect were generated from a 20 m resolution DEM which was

derived from topographical maps with a scale of 1:25,000. In doing so, standardization was

performed on the criteria. Standardization is important stage in MCDA approach and

defined ‘‘a process that transforms and rescales the original criteria into comparable units’’

(Gorsevski et al. 2012, p. 288). This technique is an extension of the classic binary logic,

which enables the definition of sets without sharp boundaries and allows elements to be

partially assigned to a particular set. A fuzzy set is essentially a set whose members have

degrees of membership ranging between 0 and 1, as opposed to a classic binary set in

which each element must have a membership degree of either 0 or 1 (Malczewski 2004). In

this particular landslide analysis for Urmia lake basin, the criteria were represented by

separate GIS dataset layers with memberships of different potential classes and were

subsequently standardized using the maximum eigenvectors approach on a 0–1 scale.

3 Methods

3.1 Selection of evaluation criteria

Evaluation criteria objectives and attributes need to be identified with respect to the par-

ticular situation under consideration. The set of criteria selected should adequately rep-

resent the decision-making environment and contribute towards the final goal (Prakash

2003). There are no universal guidelines for selecting parameters that influence landslides

in susceptibility mapping (Yalcin 2008; Feizizadeh et al. 2012b), but landslides exhibit a

combination of two or more types of movements, resulting in a complex type (Varnes

1984). They are triggered by a number of external factors, such as intense rainfall,

earthquake shaking, water level change, storm waves, rapid stream erosion, etc. (Dai and

Lee 2002). In addition, extensive human interference in hill slope areas for the construction

of roads, urban expansion along the hill slopes, deforestation and rapid change in land use

contribute to instability. This makes it difficult, if not impossible, to define a single

methodology to identify and map landslides, to ascertain landslide hazards and to evaluate

the associated risk (Guzzetti et al. 2005; Das et al. 2011). In this study, topography,

geology, climate, vegetation and anthropogenic factors were selected based on expert

knowledge, on the basis of field studies related to active landslides. The results of the

susceptibility map are determined by factors with high local representation such as lin-

eaments and turned to have artefacts that reduce its reliability. There are also studies that

used natural (lithology, lineament) and artificial (roads and other engineering structures),

or causal (e.g. slope, lithology) and triggering (rain, seismicity, etc.) factors together

(Ayalew and Yamagishi 2005; Yalcin 2008). The selection of the nine causal factors in this

study is based on these four criteria, as well as considering general literature inputs and

data availability (Ayalew and Yamagishi 2005). Lithology, DEM, slope, aspect, land

cover, precipitation, distance to streams and the distance to roads and faults are the factors

that are most often used for susceptibility mapping (Yalcin 2008; Thanh 2008; Chauhan

et al. 2010; Feizizadeh et al. 2011; Oh and Pradhan 2011; Bai et al. 2011).

Nat Hazards (2013) 65:2105–2128 2109

123

3.2 Analytical hierarchy process

The AHP method (Saaty 1977, 1980; Saaty and Vargas 1991) is a well-known method of

the multicriteria technique, which has been incorporated into GIS-based suitability pro-

cedures (Marinoni 2004; Jankowski and Richard 1994). Quantitative and qualitative

information about decision-making problems can be organized using the AHP method

(Saaty 1980; Malczewski 1999). The AHP can assist in identifying and weighting selection

of criteria and expediting the process of decision-making (Sener et al. 2010; Yahaya et al.

2010). The AHP is a multi-attribute weighting method, used to derive ratio scales from

paired comparisons and to introduce objectivity in weight assignment. It provides an

effective means to deal with complex decision-making by assisting with identifying and

weighting selection criteria; it is a useful mechanism for checking the consistency of the

evaluation measures and alternatives suggested by the decision-maker. The input can be

obtained from actual measurements such as price, weight, etc., or from subjective opinion

such as preference and expert judgement (Kritikos and Davies 2011). ‘‘The AHP method

reduces the complexity of a decision problem to a sequence of pairwise comparisons,

which are synthesized in a ratio matrix that provides a clear rationale for ordering the

decision alternatives from the most to the least desirable. Specifically, the process builds a

hierarchy of decision criteria. Through the pairwise comparison of each possible criterion

pair, a relative weight for each decision criterion within the hierarchy is produced. The

development of AHP pairwise comparison is based on the rating of relative preferences for

two criteria at a time. Each comparison is a two-part question determining which criterion

is more important and to what extent, using a scale with values from the set: {1/9, 1/8, 1/7,

1/6, 1/5, 1/4, 1/3, 1/2, 1, 2, 3, 4, 5, 6, 7, 8, 9}. The values range from 1/9 representing the

least important (than), to 1 for equal importance and to 9 for the most important (than),

covering all the values in the set. The AHP comparison matrix consists of an equal number

of rows and columns, where scores are recorded on one side of the diagonal, while values

of 1 are placed in the diagonal of the matrix’’ (Gorsevski et al. 2006, p. 127). The AHP

typically involves establishing a graphical representation of problem as a hierarchy,

weighting the elements at each level of the hierarchy and calculating the weights (Phua and

Minowa 2005; Yahaya et al. 2010). Based on the AHP approach, the weights are deter-

mined by normalizing the eigenvector which corresponds to the largest eigenvalue of the

ratio matrix (Malczewski 1999; Gorsevski et al. 2006). Since human judgment can violate

the transitivity rule and thus cause an inconsistency, the consistency ratio (CR) is computed

to check the consistency of the conducted comparisons. In case of high inconsistency, the

most inconsistent judgments can be revised (Gorsevski et al. 2006). After the weights are

determined through the pairwise comparison method, the resulting evaluation scores are

used to order the decision alternatives from the most to the least desirable, followed by an

aggregation criterion technique (Jiang and Eastman 2000; Gorsevski et al. 2006). Because

of its simplicity and robustness in obtaining weights and integrating heterogeneous data,

the AHP has been used in a wide variety of applications, including multi-attribute decision-

making, total quality management, suitability analysis, resource allocation, conflict man-

agement, design and engineering (Vargas 1990; Chen and Hwang 1992; Jiang and Eastman

2000; Vaidya and Kumar 2006; Gorsevski et al. 2006). However, it should be noted that

the AHP has been criticized for its inability to adequately handle the ambiguity and

imprecision associated with the conversion of linguistic labels attached to the ratio scale, to

crisp numbers used in the comparison matrix. The other criticisms concern the axiomatic

foundation of the method, the correct meaning of priorities, the measurement scale and the

rank reversal (Lootsma 1993; Barzilai 1998; Leskinen 2000; Mikhailov 2003; Gorsevski

2110 Nat Hazards (2013) 65:2105–2128

123

et al. 2006). Despite these shortcomings, the AHP has been widely used for practical

applications and integrated with other methodologies such as fuzzy sets to represent human

judgments and capture their inconsistencies (Gorsevski et al. 2006).

3.3 Weighted linear combination

The WLC method is one of the most commonly used GIS-MCDA (Malczewski 2000). The

WLC technique is a popular method that is customized in many GIS and is applicable for

the flexible combination of maps. The tables of scores and the map weights can be adjusted

to reflect the judgment of an expert in the domain of the application being considered

(Ayele 2009). This method initially requires the standardization of the classes in each

factor to a common numeric range. The class rating within each factor was based on the

relative importance of each class according to field observations in the study area and

existing literature, indicating certain conditions as the most favourable to slope failure

(Kritikos and Davies 2011). WLC can be considered as a hybrid between qualitative and

quantitative methods (Ayalew et al. 2004). This method represents the simplest procedure

that aggregates criteria, once the criterion scores have been standardized and the weights

have been computed to form a single score of evaluation (Voogd 1983). In the WLC

method, each criterion is multiplied by its weight from the pairwise comparison and the

results are summed:

S ¼X

i

wili ð1Þ

In this formula, S represents the final score, wi represents the weight of the criterion i, and

li represents the criterion standardized score (Gorsevski et al. 2006). Weights can have a

tremendous influence on the solution. Due to the criterion weights being summed to one,

the final scores of the combined solution are expressed on the same scale. Also, weights

given to each criterion determine the trade-off level relative to the other criteria, which

implies that high scores and weights from standardized criteria can compensate for low

scores from other criteria. However, when scores from standardized criteria are low while

the weights are high, they can only weakly compensate for the poor scores from other

criteria (Jiang and Eastman 2000; Gorsevski et al. 2006; Gorsevski and Jankowski 2010).

WLC (or simple additive weighting) is based on the concept of a weighted average. The

decision-maker directly assigns the weights of ‘relative importance’ to each attribute map

layer. A total score is then obtained for each alternative by multiplying the weight assigned

to each attribute by the scaled value given to the alternative on that attribute, and summing

the products of all attributes. When the overall scores are calculated for all of the alter-

natives, the alternative with the highest overall score is chosen. The method can be

operationalized using any GIS system with overlay capabilities. The overlay techniques

allow the evaluation criterion map layers (input maps) to be combined, in order to

determine the composite map layer (output map) (Malczewski 2004).

3.4 Ordered weighted averaging

The OWA operators were introduced by Yager in 1988. OWA is a class of multicriteria

operators which was given quantifier guided aggregation in 1996 (Yager 1988, 1996).

OWA is a method which involves two sets of weights including criterion importance

weights and order weights (Malczewski 2006a). An important weight is assigned to a given

criterion (attribute) for all locations in a study area to indicate its relative importance

Nat Hazards (2013) 65:2105–2128 2111

123

(according to the decision-maker’s preferences) in the set of criteria. The order weights are

associated with the criterion values on a location-by-location (object-by-object) basis.

They are assigned to a location’s attribute values in decreasing order without considering

which attribute the value comes from. The order weights are central to the OWA com-

bination procedures. Yager (1988) proposed OWA as a parameterized family of combi-

nation operators. For a given set of n criterion (attribute) maps, an OWA operator can be

defined as the following function OWA: In ? I, where I = [0, 1] that is associated with a

set of order weights V = [v1, v2, …, vn] so that vj e [0,1] for j = 1, 2,…, n andPnj¼1¼ 1v j ¼ 1 given a set of standardized criterion value Ai = [ai1, ai2…, ain] for

i = 1,2,…m, where aij e [0,1] is associated with the location (e.g. cell, polygon, line,

point), the OWA operator is defined as follows (Boroushaki and Malczewski 2010):

OWAiðai1; ai2; . . .; ainÞ ¼Xn

j¼1

vjzij ð2Þ

where zai1 C zi2 C �� C zin is the sequence obtained by reordering the criterion values

ai1, …, ain. With different sets of order weights v, one can generate a wide range of OWA

operators including the three cases used in this article: WLC, Boolean overlay combination

(‘‘AND’’) and (‘‘OR’’) by changing the set of order weights v (Yager 1988; Malczewski

2006b; Boroushaki and Malczewski 2010).

The OWA combination operator in Eq. (2) ignores the fact that most of the GIS-based

decision-making problems require a set of different weights to be assigned to criterion

maps layers. In order to extend the conventional OWA approach, it is necessary to fuse the

‘criterion weights’ (importances), W, into the OWA procedure. Yager (1997) proposed a

criterion weight modification approach for the inclusion of criterion weights into the OWA

operator as follows (Boroushaki and Malczewski 2010):

Vj ¼ Q

PiI¼1 uIPnI¼1 uI

!� Q

Pj�1I¼1 uIPnI¼1 uI

!ð3Þ

where uj is the reordered jth criterion weight, wj, according to the reordered zij Considering

Q(p) = px for x [ 0, Eq. (3) can be simplified to:

Vj ¼Pi

I¼1 uIPnI¼1 uI

!x

�Pj�1

I¼1 uIPnI¼1 uI

!x

ð4Þ

Accordingly, given the sets of criterion weights, W, and order weights, v, the OWA

operator can be defined as:

OWAi

Xn

j¼1

¼Pi

I¼1 uIPnI¼1 uI

!x

�Pj�1

I¼1 uIPnI¼1 uI

!x !zij ð5Þ

OWA provides a tool for generating a wide range of decision strategies in a decision

strategy space, by applying a set of order weights to criteria that are ranked in ascending

order on a pixel-by-pixel basis. The number of order weights is equal to the number of

criteria and must sum to one. The position of a set of order weights can be identified in a

decision strategy space based on the concepts of trade-off and risk (Yager 1988; Jiang and

Eastman 2000). Trade-off indicates the degree to which a low standardized value on one

layer can be compensated for by a high standardized value on other considered criteria risk

2112 Nat Hazards (2013) 65:2105–2128

123

refers to how much each criterion affects the final solution (Jiang and Eastman 2000;

Malczewski 2006a; Robinson et al. 2010; Feizizadeh et al. 2012a).

3.5 Calculation of criterion weights

Criterion weights are the weights assigned to the objective and attribute maps (Meng et al.

2011; Feizizadeh and Blaschke 2012a). Criteria weights represent the influence of each

criterion in the model on the distribution of mesquite. In the MCDA procedure, AHP

(Saaty 1980) is often applied for the elicitation of criteria weights. AHP provides a hier-

archical structure by reducing multiple criteria into a pairwise comparison method for

individual or group decision-making and allows the use of quantitative (objective) and

qualitative (subjective) information (Gorsevski et al. 2006; Malczewski 1999; Saaty 1980;

Gorsevski et al. 2012). To apply the AHP approach described above, it is necessary to

break a complex unstructured problem down into its component factors. These factors must

then be arranged in a hierarchic order and assigned numerical values to enable subjective

judgment of the relative importance of each factor. The judgements are then synthesized, in

order to determine the priorities to be assigned to these factors (Saaty and Vargas 1991). In

the construction of a pairwise comparison matrix, each factor is rated against every other

factor by assigning a relative dominant value between 1 and 9 to the intersecting cell (see

Table 1). In this technique, the effects of each criteria to the susceptibility of landslides

relative to each other were determined by evaluating the preferences in the effects of the

criterions to the landslide susceptibility map. Normally, the determination of the values of

the criterions relative to each other is subject to the choices of the decision-maker. In this

study, we utilized the AHP’s ability to incorporate different types of input data and the

pairwise comparison method for comparing two criterions at the same time. However, both

the comparison of the criterions relative to each other and the determination of the decision

alternatives, namely the effect values of the sub-criteria of the criterions (see Table 2 for

weights of sub-criteria), were based on the comparison of LSM criterions. Respectively to

the implementation of the pairwise comparison matrix, experts’ opinions were asked to

calculate the relative importance of the factors and criteria involved. Consequently, the

weight values were determined using AHP pairwise matrix for the datasets used (see

Table 3). In the next step, the CR (Saaty 1977) was calculated in order to determine

whether the pairwise comparisons were consistent or not. One of the strengths of the AHP

method is that it allows for inconsistent relationships while at the same time, providing a

CR as an indicator of the degree of consistency or inconsistency (Forman and Selly 2001;

Chen et al. 2009). Therefore, we implemented the AHP method in this study with the

option to let the user define an acceptable CR threshold value. If the CR [ 0.10, it is

Table 1 Scales for pairwisecomparisons (Saaty and Vargas1991)

Intensity of importance Description

1 Equal importance

3 Moderate importance

5 Strong or essential importance

7 Very strong or demonstrated importance

9 Extreme importance

2, 4, 6, 8 Intermediate values

Reciprocals Values for inverse comparison

Nat Hazards (2013) 65:2105–2128 2113

123

Table 2 Pairwise comparison matrix, factor weights and consistency ratio of the data layers used

Factors 1 2 3 4 5 6 7 8 Eigenvalues

Lithology

(1) Altered zone 1 0.045

(2) Metamorphic–Plutonic 1 1 0.036

(3) Plutonic 3 3 1 0.020

(4) Volcanic 6 5 7 0.101

(5) Metamorphic–Volcanic 6 5 4 1 1 0.120

(6) Volcanic–Sedimentary 5 3 5 4 4 1 0.200

(7) Sedimentary–Volcanic 7 6 8 2 2 1 1 0.208

(8) Sedimentary 8 6 8 2 2 1 1 1 0.270

Consistency ratio: 0.061

Precipitation (mm)

(1) 250[ 1 0.083

(2) 251–300 3 1 0.098

(3) 301–350 4 3 1 0.116

(4) 350–400 7 4 1/3 1 0.301

(5) 401–485 8 3 7 5 1 0.402


Land use/cover

(1) Settlement 1 0.053

(2) Orchard and croplands 3 1 0.067

(3) Dry-farming and pasturelands

8 7 1 0.235

(4) Bare soil 9 8 3 1 0.320

(5) Rock bodies 9 8 3 3 1 0.325


Slope (�)

(1) 0–10 1 0.110

(2) 10.1–20 3 1 0.173

(3) 20.1–30 4 3 1 0.393

(4) 30.1–40 3 3 1/3 1 0.062

(5) 40.1\ 1/3 1/4 1/6 1/4 1 0.085


Distance to fault (m)

(1) 0–1,000 1 0.514

(2) 1,001–2,000 1/3 1 0.224

(3) 2,001–3,000 1/5 1/3 1 0.126

(4) 3,001–4,000 1/7 1/5 1/2 1 0.085

(5) 4,000\ 1/5 1/2 2 3 1 0.050


Distance to stream (m)

(1) 0–50 1 0.514

(2) 51–100 1/3 1 0.224

(3) 101–150 1/5 1/3 1 0.126

2114 Nat Hazards (2013) 65:2105–2128

123

important to be careful to accept the resulting weights without changing the inputs to the

pairwise comparison matrix and also to ensure that the matrix really reflects the user’s

beliefs and does not contain errors (Bodin and Gass 2003; Feizizadeh and Blaschke

2012a, b). In our study, the resulting CR for the pairwise comparison matrix for nine

dataset layers was 0.053 (see Table 3). This value indicates that the comparisons of

characteristics were perfectly consistent and that the relative weights were appropriate to

be subsequently used in the landslide susceptibility model.

4 Results

4.1 Landslide susceptibility maps

Three landslide susceptibility maps were produced based on GIS-MCDA techniques

including WLC (Fig. 3), OWA (Fig. 4) and AHP (Fig. 5). The landslide susceptibility

mapping was evaluated qualitatively to ensure the selection of the most appropriate method

and to improve the prediction accuracy of the landslide susceptibility map. For the AHP-

based landslide susceptibility map, high susceptible zones cover about 4.47 % (944 km2)

Table 2 continued

Factors 1 2 3 4 5 6 7 8 Eigenvalues

(4) 151–200 1/7 1/5 1/2 1 0.085

(5) 200\ 1/5 1/2 1/6 1/4 1 0.050


Distance to roads (m)

(1) 0–25 1 0.269

(2) 26–50 4 1 0.255

(3) 51–75 4 2 1 0.249

(4) 76–100 4 2 1 1 0.135

(5) 100\ 3 2 1 1 1 0.092


Aspect

(1) Flat 1 0.036

(2) North 9 1 0.053

(3) East 1 1/8 1 0.104

(4) West 4 1/7 3 1 0.269

(5) South 9 7 7 7 1 0.511


Elevation (m)

1,260–1,400 1 0.076

1,401–1,800 9 1 0.239

1,801–2,500 9 8 1 0.393

2,501–3,000 8 7 7 1 0.173

3,001–3,710 7 1/7 1/6 1/5 1 0.119


Nat Hazards (2013) 65:2105–2128 2115

123

of the total area while about 61.56 % (12,197.29 km2) was classified as being moderately

susceptible and 31.25 % (6,224.71 km2) was classified as being a low susceptible zone.

2.72 % of the case study area (541.85 km2) was classified as being unsusceptible to

landslides. In the OWA-based landslide susceptibility map, the high susceptible zones

cover about 4.58 % (913.61 km2) of the total area while about 62.42 % (12,428.29 km2)

was classified as being the moderately susceptible and 30.28 % (6,028.58 km2) was

Table 3 Pairwise comparison matrix for dataset layers of landslide analysis

Factors 1 2 3 4 5 6 7 8 9 Eigenvalues

(1) Aspect 1 0.025

(2) Distance to road 1/5 1 0.036

(3) DEM 1/2 1/3 1 0.020

(4) Distance to stream 1/3 1/3 1/3 1 0.112

(5) Distance to fault 1/3 1/5 1/5 1/3 1 0.124

(6) Slope 7 1/5 9 1/3 1/4 1 0.141

(7) Land use 8 6 1/5 1/5 1/3 1/3 1 0.160

(8) Precipitation 8 6 7 7 4 3 1/5 1 0.172

(9) Lithology 9 7 1/3 8 7 4 1/5 8 1 0.210


Fig. 3 Landslide susceptibility map derived from weighted linear combination

2116 Nat Hazards (2013) 65:2105–2128

123

classified as being low susceptible. 2.72 % of the study area (541.79 km2) was classified as

being unsusceptible to landslides. Lastly, the WLC-derived landslide susceptibility map

yields high susceptible zones of about 3.05 % (607.61 km2) of the total area while about

63.57 % (12,655.33 km2) was classified as being moderately susceptible and 33.13 % of

the case study area (6,594.58 km2) was classified as being a low susceptible zone. 0.25 %

of the study area (51.32 km2) was classified as unsusceptible to landslides. Detailed results

are shown in Table 4.

4.2 Validation of models used

4.2.1 Comparison of landslide susceptibility maps with known landslide areas

In order to quantitatively evaluate which landslide susceptibility method is the most

beneficial, an existing landslide inventory dataset was used for a comparison. It includes

132 known landslide events which are compared with the respective susceptibility maps

that are derived from the AHP, OWA and WLC methods (see Table 4).

For the AHP method, the comparison shows that about 21.2 % of the known landslides

lie within the high susceptibility category, while approximately 75.7 % of known land-

slides fall into the moderate susceptibility category and about 3.1 % of landslides lie within

the low susceptible class. Furthermore, no known landslide event is observed in the cat-

egory ‘no susceptibility’.

Fig. 4 Landslide susceptibility map derived from ordered weighted average

Nat Hazards (2013) 65:2105–2128 2117

123

For the OWA method, the comparison shows that the high susceptibility category

covers about 20.16 % of the known landslides, while the category ‘moderate susceptibility’

covers about 76.74 % and the ‘low susceptibility’ category covers about 3.1 % of the

known landslide events, respectively. None of the known landslide events falls into the

category ‘no susceptibility’.

For the WLC method, the comparison shows that the high susceptibility category covers

about 10.57 % of the known landslides in the study area, while about the 86.33 % of the

known landslides fall into the moderate susceptibility category, and the category of low

susceptibility covers about 3.1 % of the known landslides. No landslides were observed in

the ‘no susceptibility’ category.

4.2.2 Uncertainty analysis based on Dempster-Shafer theory

The Dempster-Shafer theory (DST) was used to carry out uncertainty analysis of GIS-

MCDA. The DST of evidence, which was originally based on Dempster’s work on the

generalization of the Bayesian theory (Dempster 1967), and was formalized by Shafer

(1976), can provide a mathematical framework for the description of incomplete knowl-

edge (Park 2011). The DST was used to measure the uncertainty in the landslide sus-

ceptibility maps. Based on the DST (belief) approach, the uncertainty is the difference to

the ‘plausibility’. The ignorance value can be used to represent the lack of evidence

(complete ignorance is represented by 0). The belief function shows the spatial distribution

of the belief or support for landslide susceptibility. In the context of the uncertainty

assessment for the WLC method, the belief function reveals that certainty ranges between

Fig. 5 Landslide susceptibility map derived from analytic hierarchy process

2118 Nat Hazards (2013) 65:2105–2128

123

0.15 and 0.67 (see Fig. 6). Uncertainty analysis results for OWA show a certainty range of

0.24–0.89 (See Fig. 7). The uncertainty analysis for the AHP method results in a certaintyrange of 0.22–0.87 (see Fig. 8). The detailed uncertainty results for all three MCDA

methods are shown in Table 4.

5 Discussion

Mass movement and landslide predisposing factors are generally assumed to be similar to

those observed in the past. If this assumption holds true, one can predict future slides

occurring in a non-specified time span. Since most factors listed in Table 4 are relatively

stable, this study—and many others—uses this hypothesis. The only critical factor is land

use which may change more rapidly. A certain risk would be climate change which may

result in changes in the precipitation regime.

The GIS-MCDA methods provide a framework to handle different views and compo-

sitions of the elements of a complex decision problem, organize the elements into a

hierarchical structure and study the relationships among the components of the problem

(Malczewski 2006a, b). Results of this research indicated that the AHP method performed

best compared to the WLC and OWA methods. In particular, the AHP method leads to

more accurate maps. However, when analysing the landslide susceptibility maps alone, the

OWA-derived results are not much different. Through the validation exercise with known

existing landslides, the AHP clearly performed best. For the uncertainty analysis experi-

ments based on the DST method, the OWA method revealed the highest certainty. Both

methods—and their respective map results—performed better than the landslide suscep-

tibility maps derived from the WLC method. We should note that the differing accuracies

are due to the different decision rules of the respective MCDA operators. Naturally, a

variety of decision rules will result in various MCDA accuracies. Jiang and Eastman

(2000) describe two of the most common procedures in MCDA that are used to aggregate

criteria in a spatial decision support system. The first procedure is the Boolean overlay,

which involves a combination of maps (criteria) by logical operators such as intersection

Table 4 Comparison landslide susceptibility with landslides in study area

MCDA Susceptibilitycategory

Area undercategory (%)

Comparison withlandslides area (%)

Results of uncertaintyanalysis using DST

AHP High susceptibility 4.47 21.2 0.85–0.87

Moderate susceptibility 61.56 75.7 0.65–0.84

Low susceptibility 31.25 3.1 0.46–0.64

No susceptibility 2.72 0 0.22–0.45

OWA High susceptibility 4.58 20.16 0.85–0.89

Moderate susceptibility 62.42 76.74 0.65–0. 84



WLC High susceptibility 3.05 10.57 0.64–0.67

Moderate susceptibility 63.57 86.33 0.56–0.63



Nat Hazards (2013) 65:2105–2128 2119

123

(AND) or union (OR). The second procedure is the WLC, which involves numeric stan-

dardization of evaluation criteria aggregated by the weighted average. Indeed, applications

of both aggregation techniques have been increasingly implemented in LSM, using various

modelling frameworks (Carrara et al. 1995; Barredo et al. 2000; van Westen et al. 2000;

Ayalew et al. 2004; Lee and Choi 2004; Gorsevski et al. 2006; Komac 2006; Gorsevski and

Jankowski 2010).

Among the GIS-MCDA methods, the AHP gained high popularity due to the ease in

obtaining the criterion weights, as well as its capacity for integrating heterogeneous data. It

is therefore applied in a wide variety of decision-making problems. It is especially useful

in situations where it is impractical or even impossible to specify the exact relationships

between large numbers of evaluation criteria (Deng 1999; Chen et al. 2009). Due to the

large amount of criteria and the heterogeneity of data sources, the uncertainty of the results

remains typically unclear. Even small changes in decision weights and methods may have

a significant impact on the rank ordering of the criteria and may subsequently change the

results (Feizizadeh et al. 2012a, b; Feizizadeh and Blaschke 2012b). In our research, the

WLC resulted in relatively low accuracies in both validation and uncertainty analyses. It is

obvious that a critical step in GIS-MCDA is the assignment of weights to the different

criteria, according to their significance. This step involves the selection of a suitable

weighting method and the quest for a high objectivity. The incorrect specification of

weights is a common error in the application of WLC to spatial decision problems

(Malczewski 2000; Kritikos and Davies 2011). Furthermore, the application of WLC

requires the standardization of different classes within each factor to a common numeric

range (e.g. 0–100). This standardization addresses relative importance of each class in

Fig. 6 Uncertainty analysis results of map derived from weighted linear combination

2120 Nat Hazards (2013) 65:2105–2128

123

landslide susceptibility (the higher the more important). This is achieved by a simple linear

scale transformation. The uncertainty here stems from the fact that we assume that the

importance increases linearly in each class for example with increase in slope or decrease

in distance from roads and faults (Kritikos and Davies 2011).

The level of trade-off is considered as an important issue in terms of the accuracy

assessment for GIS-MCDA. In weighted combination methods, a factor with a high cri-

terion weight can trade off or compensate for poor weights on other factors. The WLC

method is situated in the middle of the continuum ranging from the ‘MIN’ (Boolean

‘AND’ operator) to ‘MAX’ (Boolean ‘OR’ operator), which indicates full trade-off among

criteria. The OWA method can be used to select any degree of trade-off among criteria,

ranging from no trade-off to full trade-off, depending on the decision-making strategy.

Therefore, the Boolean overlay represents the extreme case with no trade-off. The Boolean

‘AND’ operator represents the ‘MIN’ risk decision-making, while the Boolean ‘OR’

operator represents the ‘MAX’ risk decision-making in strategy space. The WLC method

averages the risk in decision-making, with full trade-off among criteria (Gorsevski et al.

2012; Feizizadeh and Blaschke 2012c).

OWA is a relatively new MCDA method that is analogous to WLC but which considers

two sets of weights which provide additional flexibility for OWA. In this method, the first

set of weights controls the relative contribution of specific criterion while the second set of

weights controls the order of the aggregation of the weighted criteria (Jiang and Eastman

2000; Malczewski 1999, 2006a; Gorsevski et al. 2012). ‘‘The appeal of OWA is that by

reordering and changing criterion parameters, one can generate a wide range of different

solution maps and predictive scenarios. Unlike the Boolean overlay where the intersection

Fig. 7 Uncertainty analysis result of map derived from ordered weighted average

Nat Hazards (2013) 65:2105–2128 2121

123

‘AND’ operator represents the lowest risk while the union ‘OR’ represents the highest risk

in the decision-making, the OWA method can obtain a full spectrum of risk scenarios

bounded between the intersection ‘AND’ and the union ‘OR’ operators’’ (Gorsevski et al.

2012, p. 288). For instance, within the OWA context, the equal weights WLC method

represents an ‘‘average risk in decision-making’’ (Gorsevski et al. 2012, p. 288). The OWA

method can select risk dynamically according to the decision-making strategy and obtain

any results from ‘MIN’ risk to ‘MAX’ risk with the appropriate trade-off (Chen and Zhu

2010). The nature of OWA depends on its capability of implementing different combi-

nations of operators, by specifying an appropriate set of order weights. OWA operators

have limited applications in situations involving a large set of evaluation criteria (Yager

1996; Malczewski 2006a). Under a complex spatial decision situation, the decision-maker

might find it difficult or impossible to provide precise numerical information for the OWA

parameters (Malczewski 2006a). According to Eastman (2001), results generated by OWA

lead to a decision rule that falls in a triangular decision space where the risk aversion

decision rule is generated by using the ‘AND’ operator, while a risk taking decision rule is

generated by the ‘OR’ operator. ‘‘The intermediate solution between the ‘AND’ and ‘OR’

operators are part of the decision space. The WLC solution, which is obtained by applying

equal ordered weights, is located exactly between the ‘AND’ and the ‘OR’ operator, while

the ‘MIDAND’ and the ‘MIDOR’ are located between the ‘AND’ and the WLC

and between the WLC and the ‘OR’ in the triangular decision space’’ (Gorsevski and

Jankowski 2010, p. 1016). WLC is a technique where criteria are standardized to a

common numeric range and then combined by the means of a weighted average to produce

a continuous mapping of suitability. WLC is particularly suited when weighting similar

Fig. 8 Uncertainty analysis result of map derived from analytic hierarchy process

2122 Nat Hazards (2013) 65:2105–2128

123

criteria. In the case of differentiated criteria, the sole use of WLC will increase the levels of

subjectivity in the decision-making process (Feizizadeh et al. 2012a).

AHP is also used to break up criteria into clusters with common characteristics. This

combination approach utilizes the best features of each MCDA technique to improve the

decision-making process. Our results show that AHP is an improvement in the final

solution, yet the presented approach showed better accuracy in the prediction of the

landslide susceptibility. Indeed, the AHP can be used as a method for data combination.

However, the AHP method is well known as a method of criteria ranking and allows a

degree of subjectivity in the pairwise comparisons between the criteria. Furthermore, the

CR is not affected by any misconception regarding the significance of various criteria. As a

result, any incorrect perception on the role of the different slope-failure criteria can be

easily conveyed from the expert’s opinion into the weight assignment (Kritikos and Davies

2011). In an effort to deal with this source of uncertainty, Feizizadeh and Blaschke (2012b)

presented a new approach to improve the accuracy of GIS-OWA-MCDA by analysing how

changes in criteria weights affect evaluation outcomes spatially and quantitatively. They

indicate that the further improvement of the accuracy of GIS-based MCDA can be

achieved by integrating Monte Carlo Simulation for sensitivity analysis of the weights

derived from AHP and assessing uncertainty using DST.

Classification of susceptibility (susceptibility classes) is another source of uncertainty

whereby the latter originates from the classification of the final pixel values representing

the spatial distribution of susceptibility by specified classes. As there are no statistical

rules, which can guide the classification of continuous data automatically, the process of

transforming continuous data into two or more categories remains unclear in LSM (Kri-

tikos and Davies 2011). To deal with this gap, most researchers use their expert opinions to

develop class boundaries (Ayalew et al. 2004; Kritikos and Davies 2011). The natural

breaks classification method (or Jenks optimization) used in this study groups values within

a class, generating classes of similar values separated by breakpoints. The method seems to

work well with values that are neither evenly distributed nor tend to accumulate at one end

of the distribution. Other methods such as quantile classification, equal interval, equal area

and standard deviation could each be applied to generate the same classes with some

variations. The issue here is that the boundaries between susceptibility classes are unclear

and subject to uncertainty. Especially when we interpret values close to each class

boundary (e.g. values between ‘‘High’’ and ‘‘Very high’’ susceptibility), it is important to

consider the statistical assumptions underlying the classification process, before any

decisions are made based on the resulting map (Kritikos and Davies 2011).

6 Conclusions

When using GIS-MCDA methods, it is evident that each method has its advantages and

inherent limitations that must be fully understood and accepted by the decision-maker

before applying it (Kritikos and Davies 2011). In this respect, the methods and the

respective decision rules need to be analysed in terms of their usefulness and appropri-

ateness. Based on this idea, in our research, we compared the accuracy of three major GIS-

MCDA methods. These investigations could reveal the capabilities of the three GIS-

MCDA methods for LSM. In addition, we investigated the uncertainty of the respective

results. There are already some other studies on mapping landslide susceptibility through

the use of GIS-MCDA (Gorsevski et al. 2006; Ayalew et al. 2004, 2005; Guzzetti et al.

2005; Komac 2006; van Westen et al. 2006; Gorsevski and Jankowski 2008, 2010;

Nat Hazards (2013) 65:2105–2128 2123

123

Feizizadeh et al. 2011, 2012b). Some of them used multicriteria evaluation (MCE), which

is the most fundamental approach in decision-making (Malczewski 1999). However, the

respective resulting accuracies of MCDA for LSM are rarely studied. This also holds true

for uncertainty analyses of the results. The authors are confident that the outcome of this

research is a) methodologically important to the advancement of MCDA methodologies

and b) for analysing in this particular regional study which landslide susceptibility method

is most beneficial. Particularly having in mind the readership of this journal, we may

conclude that landslide susceptibility maps can increasingly be considered as base map for

decision-making when aiming to avoid or reduce the impacts of future hazards. In this

respect, it is critical to understand the capability and the specifics of each method. Only

then, we can ensure the usefulness and appropriateness of landslide susceptibility maps.

We may further conclude that in LSM process, classification and zoning are dependent

on topography, geology, geotechnical properties, climate, vegetation and anthropogenic

factors such as development and the clearing of vegetation (Fell et al. 2008). Based on the

comparative results of the landslide inventory and the landslide susceptibility maps, high

susceptible zones are demarcated. These zones include most of the known landslides that

occurred on unstable slopes over the last several years. The three obtained landslide

prediction maps were not only accomplished for the sake of comparison. We will provide

all three versions with respective explanations to the responsible authorities in the East

Azerbaijan province for risk management. The information provided by these maps shall

help citizens, planners and engineers to reduce losses caused by existing and future

landslides by means of prevention, mitigation and avoidance. The results are therefore

useful for explaining the driving factors of the known existing landslides, for supporting

emergency decisions and for supporting the efforts on the mitigation of future landslide

hazards in the Urmia lake basin.

Acknowledgments The authors would like to thank the reviewers for their helpful and constructivecomments on earlier versions of the manuscript and the Department of Geoinformatics (Z_GIS) Universityof Salzburg for partial financial support. We also appreciate the help of Dr. Hasan Ahmadzadeh, Universityof Tabriz, for his help and in particular for providing rights for Fig. 2. This work was carried out as part of aPhD study funded by the Iranian Ministry of Science, Research and Technology and including a studyperiod at the University of Salzburg.

References

Atkinson PM, Massari R (2011) Autologistic modelling of susceptibility to landsliding in the CentralApennines, Italy. Geomorphology 130:55–64

Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibilitymapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31

Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weightedlinear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides1:73–81

Ayalew L, Yamagishi H, Marui H, Kanno T (2005) Landslides in Sado Island of Japan: part II. GIS-basedsusceptibility mapping with comparisons of results from two methods and verifications. Eng Geol81:432–445

Ayele S (2009) Slope instability and hazard zonation mapping using remote sensing and GIS techniques inAbay Gorg (Gohatsion dejen) central Ethiopia. Faculty of Sciences, University of Addis Ababa, Masterthesis

Bai S, Lu G, Wang J, Zhou P, Ding L (2011) GIS-based rare events logistic regression for landslidesusceptibility mapping of Lianyungang, China. Environ Earth Sci 62:139–149

Banai R (1993) Fuzziness in geographic information systems: contributions from the analytic hierarchyprocess. Int J Geogr Inform Syst 7(4):315–329

2124 Nat Hazards (2013) 65:2105–2128

123

Barredo JI, Benavides A, Hervas J, Van Westen CJ (2000) Comparing heuristic landslide hazard assessmenttechniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. Int J Appl Earth Obs Geoinf2:9–23

Barzilai J (1998) On the decomposition of value functions. Oper Res Lett 22:159–170Bennett DA, Wade GA, Armstrong MP (1999) Exploring the solution space of semi-structured geographical

problems using genetic algorithms. Trans GIS 3(1):51–71Bodin L, Gass S (2003) On teaching the analytic hierarchy process. Comput Oper Res 30:1487–1497Boroushaki S, Malczewski J (2008) Implementing an extension of the analytical hierarchy process using

ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Comput Geosci 34:399–410Boroushaki S, Malczewski J (2010) Using the fuzzy majority approach for GIS-based multicriteria group

decision-making. Comput Geosci 36:302–312Carrara A, Cardinali M, Detti R, Guzzetti F, Pasqui V, Reichenbach P (1991) GIS techniques and statistical

models in evaluating landslide hazard. Earth Surf Proc Land 16(5):427–445Carrara A, Cardinali M, Guzzetti F, Reichenbach P (1995) GIS technology in mapping landslide hazard. In:

Carrara A, Guzzetti F (eds) Geographical information systems in assessing natural hazards. Kluwer,Dordrecht, pp 135–175

Carver SJ (1991) Integrating multi-criteria evaluation with geographical information systems. Int J GeogrInform Syst 5(3):321–339

Cervi F, Berti M, Borgatti L, Ronchetti F, Manenti F, Corsini A (2010) Comparing predictive capability ofstatistical and deterministic methods for landslide susceptibility mapping: a case study in the northernApennines (Reggio Emilia Province, Italy). Landslides 7:433–444

Chauhan S, Sharma M, Arora MK (2010) Landslide susceptibility zonation of the Chamoli region, GarhwalHimalayas, using logistic regression model. Landslides 7:411–423

Chen SJ, Hwang CL (1992) Fuzzy multiple attribute decision making. Springer, BerlinChen J, Zhu Q (2010) Uncertainty and decision strategy analysis of GIS-based ordered weighted averaging

method. In: International conference on information networking and automation (ICINA)Chen Y, Khan S, Paydar Z (2009) To retire or expand? A fuzzy GIS-based spatial multi-criteria evaluation

framework for irrigated agriculture. Irrig Drain 59(2):174–188Dai FC, Lee CF (2002) Landslide characteristics and slope instability modelling using GIS, Lantau Island,

Hong Kong. Geomorphology 42:213–228Das I, Sahoo S, van Westen C, Stein A, Hack R (2010) Landslide susceptibility assessment using logistic

regression and its comparison with a rock mass classification system, along a road section in thenorthern Himalayas (India). Geomorphology 114:627–637

Das S, Stein A, Kerle N, Dadhwal VK (2011) Probabilistic landslide hazard assessment using homogeneoussusceptible units (HSU) along a national highway corridor in the northern Himalayas, India. Landslides8:293–308

Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat28:325–339

Deng H (1999) Multi-criteria analysis with fuzzy pairwise comparisons. Int J Approx Reason 21:215–231Eastman JR (1997) IDRISI for windows, version 2.0: tutorial exercises. Clark University, WorcesterEastman JR (2001) Guide to GIS and image processing, release 32. Clark Labs, Clark University, WorcesterFeick RD, Hall GB (1999) Consensus building in a multi-participant spatial decision support system.

URISA J 11(2):17–23Feizizadeh B, Blaschke T (2011) Landslide risk assessment based on gis multi-criteria evaluation: a case

study Boston Abad county, Iran. J Earth Sci Eng 1:66–71Feizizadeh B, Blaschke T (2012a) Land suitability analysis for Tabriz County, Iran: a multi-criteria eval-

uation approach using GIS. J Environ Planning Manag. doi:10.1080/09640568.2011.646964Feizizadeh B, Blaschke T (2012b) Uncertainty analysis of GIS-based ordered weighted averaging method

for landslide susceptibility mapping in Urmia Lake Basin, Iran. In: Paper presented at the seventhInternational Geographic Information Science conference, September 18–21, Ohio, Columbus, USA,2012

Feizizadeh B, Blaschke T (2012c) Comparing GIS-multicriteria decision analysis for landslide susceptibilitymapping for the Urmia lake basin, Iran. In: Paper presented at the IEEE international geoscience andremote sensing symposium, Igarss, 22–27 July, Munich, Germany

Feizizadeh B, Blaschke T, Rafiq L (2011) GIS based landslide susceptibility mapping: a case study Bostanabad County, Iran. Paper presented at the International conference Geoinformatics for DisasterManagement, 03-08 May 2011, Antalya, Turkey

Feizizadeh B, Blaschke T, Nazmfar H (2012a) GIS-based ordered weighted averaging and dempster shafermethods for landslide susceptibility mapping in Urmia lake Basin, Iran. Int J Digit Earth (in press)

Nat Hazards (2013) 65:2105–2128 2125

123

http://dx.doi.org/10.1080/09640568.2011.646964

Feizizadeh B, Blaschke T, Rezaei Moghaddam M.H (2012b) Landslide susceptibility mapping for the UrmiaLake basin, Iran: a multi-criteria evaluation approach using GIS. Int J Environ Res (in press)

Feizizadeh B, Blaschke T, Nazmfar Z, Akbari E, Kohbanani (2012c) Monitoring land surface temperaturerelationship to land use/land cover from satellite imagery in Maraqeh County, Iran. J Environ PlanningManag. doi:10.1080/09640568.2012.717888

Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide suscep-tibility, hazard and risk zoning for land-use planning. Eng Geol 102:99–111

Forman EH, Selly MA (2001) Decision by objective, how to convince others that you are right. WorldScientific Publishing Co. Pte. Ltd, Singapore

Gorsevski PV, Jankowski P (2008) Discerning landslide susceptibility using rough sets. Comput EnvironUrban Syst 32:53–65

Gorsevski PV, Jankowski P (2010) An optimized solution of multi-criteria evaluation analysis of landslidesusceptibility using fuzzy sets and Kalman filter. Comput Geosci 36:1005–1020

Gorsevski PV, Jankowski P, Gessler PE (2006) An heuristic approach for mapping landslide hazard byintegrating fuzzy logic with analytic hierarchy process. Control Cybern 35:21–141

Gorsevski PV, Donevska KR, Mitrovski CD, Frizado JD (2012) Integrating multi-criteria evaluationtechniques with geographic information systems for landfill site selection: a case study using orderedweighted average. Waste Manag (Oxford) 32:287–296

Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazardassessment at the basin scale. Geomorphology 72:272–299

Intarawichian N, Dasananda S (2010) Analytical hierarchy process for landslide susceptibility mapping inlower Mae Chem watershed, Northern Thailand. Suranaree J Sci Technol 17(3):277–292

Iranian Census Centre (2007) Iranian cities population. Online available at, http://www.amar.org.irJankowski P, Nyerges T (2001a) Geographic information systems for group decision-making: towards a

participatory geographic information science. Taylor & Francis, New York, p 273Jankowski P, Nyerges T (2001b) GIS-supported collaborative decision-making: results of an experiment.

Ann As Am Geogr 91(1):48–70Jankowski P, Richard L (1994) Integration of GIS-based suitability analysis and multicriteria evaluation in a

spatial decision support system for route selection. Environ Planning 21(3):326–339Jankowski P, Nyerges TL, Smith A, Moore TJ, Horvath E (1997) Spatial group choice: a SDSS tool for

collaborative spatial decision-making. Int J Geogr Inform Sci 11(6):577–602Jiang H, Eastman JR (2000) Application of fuzzy measures in multi-criteria evaluation in GIS. Int J Geogr

Inform Sci 14:173–184Joerin F, Theriault M, Musy A (2001) Using GIS and outranking multicriteria analysis for land-use suit-

ability assessment. Int J Geogr Inform Sci 15:153–174Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and

multivariate statistics in perialpine Slovenia. Geomorphology 74(1–4):17–28Kritikos T, Davies TRH (2011) GIS-based Multi-Criteria Decision Analysis for landslide susceptibility

mapping at northern Evia, Greece. Z dt Ges Geowiss 162:421–434Kyem PAK (2004) On intractable conflicts participatory GIS applications: the search for consensus amidst

competing claims and institutional demands. Ann As Am Geogr 94(1):37–57Lee S, Choi J (2004) Landslide susceptibility mapping using GIS and the weight of-evidence model. Int J

Geogr Inform Sci 18:789–814Lei Z, Jing-feng H (2006) GIS-based logistic regression method for landslide susceptibility mapping in

regional scale. J Zhejiang Univ Sci A 7(2):2007–2012Leskinen P (2000) Measurement scales and scale independence in the analytic hierarchy process. J Multi-

Criteria Decis Anal 9:163–174Lootsma FA (1993) Scale sensitivity in the multiplicative AHP and SMART. J Multi-Criteria Decis Anal

2:87–110Makropoulos C, Butler D, Maksimovic C (2003) A fuzzy logic spatial decision support system for urban

water management. J Water Resour Planning Manag 129(1):69–77Malczewski J (1996) A GIS-based approach to multiple criteria group decision- making. Int J Geogr Inform

Syst 10(8):955–971Malczewski J (1999) GIS and multicriteria decision analysis. Wiley, TorontoMalczewski J (2000) On the use of weighted linear combination method in GIS: common and best practice

approaches. Trans GIS 4(1):5–22Malczewski J (2004) GIS-based land-use suitability analysis: a critical overview. Prog Planning 62(1):3–65Malczewski J (2006a) GIS-based multicriteria decision analysis: a survey of the literature. Int J Geogr

Inform Sci 20(7):703–726

2126 Nat Hazards (2013) 65:2105–2128

123

http://dx.doi.org/10.1080/09640568.2012.717888

http://www.amar.org.ir

Malczewski J (2006b) Multicriteria decision analysis for collaborative GIS. In: Balram S, Dragicevic S (eds)Collaborative Geographic Information Systems. Idea Group Publishing, Hershey, pp 167–185

Malczewski J, Rinner C (2005) Exploring multi-criteria decision strategies in GIS with linguistic quantifiers:a case study of residential quality evaluation. J Geogr Syst 7(2):249–268

Malczewski J, Chapman T, Flegel C, Walters D, Shrubsole D, Healy MA (2003) GIS-multi-criteria eval-uation with ordered weighted averaging (OWA): a case study of developing watershed managementstrategies. Environ Planning 35:1769–1784

MANR Ministry of Agriculture and Natural Resources (2008) Land use land cover mapping for Iran,Tehran, Iran

Marinoni O (2004) Implementation of the analytical hierarchy process with VBA in ArcGIS. ComputGeosci 30(6):637–646

Meng Y, Malczewski J, Boroushaki S (2011) A GIS-Based multicriteria decision analysis approach formapping accessibility patterns of housing development sites: a case study in Canmore, Alberta.J Geogr Inform Syst 3:50–61

Mikhailov L (2003) Deriving priorities from fuzzy pairwise comparison judgements. Fuzzy Sets Syst134:365–385

MNR, Ministry of Natural Resources, East Azerbaijan Province (2010) Landslide event report, Tabriz, IranNyerges TL, Montejano R, Oshiro C, Dadswell M (1997) Group-based geographic information systems for

transportation improvement site selection. Transp Res Part C Emerg Technol 5(6):349–369Oh HJ, Pradhan P (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for

shallow landslides in a tropical hilly area. Comput Geosci 37(9):1264–1276Park NW (2011) Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility

analysis. Environ Earth Sci 62:367–376Phua M, Minowa M (2005) A GIS-based multi-criteria decision making approach to forest conservation

planning at a landscape scale: a case study in the Kinabalu Area, Sabah, Malaysia. Landsc UrbanPlanning 71:207–222

Prakash TN (2003) Land suitability analysis for agricultural crops: A fuzzy multicriteria decision makingapproach. MSc thesis, ITC, Netherlands

Robinson TP, van Klinken DR, Metternicht G (2010) Comparison of alternative strategies for invasivespecies distribution modelling. Ecol Model 221:2261–2269

Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:231–281Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York, p 437Saaty TL, Vargas LG (1991) Prediction, projection and forecasting. Kluwer, Dordrecht, p 251Sener S, Sener E, Karaguzel R (2010) Solid waste disposal site selection with GIS and AHP methodology: a

case study in Senirkent-Uluborlu (Isparta) Basin, Turkey. J Environ Monit Assess 10:1010–1023Shafer G (1976) A mathematical theory of evidence. Princeton University Press, PrincetonStarr MK, Zeleny M (1977) MCDM—state and future of the arts. In: Starr MK, Zeleny M (eds) Multiple

criteria decision-making. North-Holland, Amsterdam, pp 5–29Thanh LN (2008) Landslide susceptibility mapping of the mountainous area in a Luoi District, Thua thien

hue province, Vietnam, PhD Thesis, Department of Hydrology & Hydraulic Engineering, University ofBrussels

Vaidya OS, Kumar S (2006) Analytic hierarchy process: an overview of applications. Eur J Oper Res169:1–29

Van Westen CJ, Soeters R, Sijmons K (2000) Digital geomorphological landslide hazard mapping of theAlpago area, Italy. Int J Appl Earth Obs Geoinf 2:51–60

Van Westen CJ, van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation-why is it still sodifficult? Bull Eng Geol Environ 65:167–184

Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, andvulnerability assessment: an overview. Eng Geol 102(3–4):112–131

Vargas LG (1990) An overview of the analytic hierarchy process and its applications. Eur J Oper Res 48:2–8Varnes DJ (1984) International association of engineering geology comm. on landslides and other mass

movements on slopes: landslide hazard zonation: a review of principles and practice, UNESCO Band63, Paris

Voogd H (1983) Multi-criteria evaluations for urban and regional planning. Princeton University, PrincetonYager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decision making.

IEEE Trans Syst Man Cybern 8:183–190Yager RR (1996) Quantifier guided aggregation using OWA operation. Int J Intell Syst 2(1):49–73Yager RR (1997) On the inclusion of importances in OWA aggregation. In: Yager RR, Kacprzyk J (eds) The

ordered weighted averaging operators: theory and applications. Kluwer, Boston, pp 41–59

Nat Hazards (2013) 65:2105–2128 2127

123

Yahaya S, Ilori C, Whanda JS, Edicha J (2010) Land fill site selection for municipal solid waste man-agement using geographic information system and multicriteria evaluation. Am J Sci Res 10:34–49

Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariatestatistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72:1–12

2128 Nat Hazards (2013) 65:2105–2128

123

GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods...

Documents

Transcript of GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods...