A comparative assessment of prediction capabilities of modified analytical hierarchy process (MAHP) and Mamdani fuzzy logic models using NetcadGIS for forest fire susceptibility mapping

Geomatics, Natural Hazards and Risk

ISSN: 1947-5705 (Print) 1947-5713 (Online) Journal homepage: />
A comparative assessment of prediction
capabilities of modified analytical hierarchy
process (M-AHP) and Mamdani fuzzy logic models
using Netcad-GIS for forest fire susceptibility
mapping
Hamid reza Pourghasemi, Masood Beheshtirad & Biswajeet Pradhan
To cite this article: Hamid reza Pourghasemi, Masood Beheshtirad & Biswajeet Pradhan
(2016) A comparative assessment of prediction capabilities of modified analytical
hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest
fire susceptibility mapping, Geomatics, Natural Hazards and Risk, 7:2, 861-885, DOI:
10.1080/19475705.2014.984247
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Vol. 7, No. 2, 861À885, />
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A comparative assessment of prediction capabilities of modified
analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models
using Netcad-GIS for forest fire susceptibility mapping
HAMID REZA POURGHASEMIy*, MASOOD BEHESHTIRADz and
BISWAJEET PRADHAN x
yDepartment of Natural Resources and Environment, College of Agriculture, Shiraz
University, Shiraz, Iran
zDepartment of Natural Resources, Sirjan Branch, Islamic Azad University, Sirjan, Iran
xDepartment of Civil Engineering, Faculty of Engineering, Geospatial Information
Science Research Center (GISRC), University Putra Malaysia, Serdang 43400, Malaysia
(Received 31 May 2014; accepted 1 November 2014)
The main purpose of this study is to assess forest fire susceptibility maps (FFSMs)
and their performances comparison using modified analytical hierarchy process
(M-AHP) and Mamdani fuzzy logic (MFL) models in a geographic information
system (GIS) environment. This study was carried out in the Minudasht Forests,
Golestan Province, Iran, and was conducted in three main stages such as spatial
data construction, forest fire modelling using M-AHP and MFL, and validation
of constructed models using receiver operating characteristic (ROC) curve. At
first, seven conditioning factors, such as altitude, slope aspect, slope angle, annual
temperature, wind effect, land use, and normalized different vegetation index,
were extracted from the spatial database. In the next step, FFSMs were prepared
using M-AHP and MFL modules in a Netcad-GIS Architect environment.
Finally, the ROC curves and area under the curves (AUCs) were estimated for
validation purposes. The results showed that the AUCs for MFL and M-AHP are

88.20% and 77.72%, respectively. The results obtained in this study also showed
that the MFL model performed better than the M-AHP model. These FFSMs
can be applied for land use planning, management, and prevention of future fire
hazards.

1. Introduction
Forests are major natural resources which play a crucial role in maintaining environmental balance. The health of forest in a given area is a true indicator of the ecological condition prevailing in that area (Saklani 2008). In general, fire is a natural
component of many forest ecosystems and cannot be avoided (Dimopoulou &
Giannikos 2001). Forest fires cause major damages to environment, human health
and property, and endanger life (Rawat 2003). Six million square kilometre of forests
has been lost around the world in less than 200 years mainly due to forest fire (Dimopoulou & Giannikos 2002). In Iran, forest fire is one of the most natural occurring
hazards. According to the ECE/FAO database (Economic Commission for Europe/
Food and Agriculture Organization) on forest fires in 1982À1995, the number of
*Corresponding author. Email:
Ó 2014 Taylor & Francis


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H.R. Pourghasemi et al.

forest fires per year is 130 and the burnt average area and its maximum is 54 km2 and
330 km2, respectively (Allard 2001). During the period 1991À1997, nearly 3063 fires
have been reported, of which 13,700 ha was burnt. In the year 1998, there were 998
fires reported and the burnt area was estimated at 206,713 ha covering mostly shrubs.
Losses were estimated at more than 5.6 million Rials (almost USD 3200 in the year
of 1998), including 8761 tons of cattle feed lost (Allard 2001). In a recent paper,
Janbaz Ghobadi et al. (2012) reported that, in the last decade, 9086 ha of the forests

have been affected by forest fire. Also, in Iran, it is difficult to control forest fire naturally; however, it is possible to map different hazard levels for minimizing fire hazards and avoid potential damage.
In the literature, several different methods and techniques for forest fire susceptibility and risk mapping have been proposed and tested. Many studies have evaluated
forest fire using geographic information system (GIS) and remote sensing (RS) technologies (Chuvieco & Congalton 1989; Prosper-Laget et al. 1995; Castro & Chuvieco
1998; Jaiswal et al. 2002; Erten et al. 2004; Wulder & Franklin 2006; Pradhan et al.
2007; Razali 2007; Saklani 2008; Chuvieco et al. 2010; Pradhan & Assilzadeh 2010;
Adab et al. 2013; Teodoro & Duarte 2013). Several studies have applied probabilistic-based models such as fuel moisture content (FMC), fire area simulator (FARSITE), and Maxent models (Chuvieco et al. 2004; Garcıa et al. 2008; Krasnow et al.
2009; Renard et al. 2012).
In the past decade, some methods, such as artificial neural networks (ANNs)
(Betanzos et al. 2002; Maeda et al. 2009; Bisquert et al. 2012; Safi & Bouroumi 2013),
fuzzy logic (Nadeau et al. 2005; Carvalho et al. 2006; Agarwal et al. 2013), and adaptive neuro-fuzzy inference system (ANFIS) (Angayarkkani & Radhakrishnan 2011),
have been proposed. Recently, new forest risk assessment methods, such as support
vector machine (SVM) (Cortez & Morais 2007; Koetz et al. 2008; Zhao et al. 2011),
decision tree methods (Stojanova et al. 2006), and random forest (Cortez & Morais
2007; Pierce et al. 2012; Leuenberger et al. 2013), were employed and their performances were assessed.
The aim of the current research is to assess forest fire susceptibility maps (FFSMs)
using modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic
(MFL) models developed in Netcad GIS 6. The assessment was performed in the
Minudasht forests situated in Golestan Province, Iran. The main difference between
this research and the approaches described in the aforementioned publications is
that an M-AHP model is applied and the result is compared with MFL model in the
study area. Also, expert opinions are used in the mentioned models for defining the
rules and conditioning factor scores in MFL and M-AHP models, respectively. This
contribution provides originality to this study.

2. Study area
The study area is located in the eastern part of Golestan Province, in the north of
Iran, between latitudes 37 000 2700 to 37 270 5300 N, and longitudes 55 140 0000 to
56 000 3900 E (figure 1). It covers an area about 1531 km2. This county shares boundaries with other Golestan Province counties such as Kalaleh county in the north and
Azadshahr county in the west (Shadman Roodposhti et al. 2014). The elevation of
the study area ranges between 100 and 2500 m above the mean sea level. The climate

of Minudasht ranges between temperate and semi-humid types. The mean annual


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863

Figure 1. Forest fire location (black and pink dots) map in the study area. Modified from
Pourtaghi et al. (2014). To view this figure in colour, please see the online version of the journal.

precipitation within the study area varies from 138 to 335 mm (Shadman Roodposhti
et al. 2014). Based on Iranian Meteorological Organization, maximum and minimum
of temperature were reported as C40 and ¡5  C, respectively. Agriculture is the main
economic activity of the region. In addition, part of the Golestan National Park is
located within the county and it is known as tropical dry forests.

3. Methodology
The overall methodology flow chart of the study is shown in figure 2. The flowchart
consists of three phases: (1) data integration and analysis, (2) forest fire susceptibility
modelling using M-AHP and MFL approaches, and (3) validation of the constructed
models using receiver operating characteristic (ROC) curve.

3.1. Data integration and analysis
In general, data collection and construction of a database of effective factors in any
study area are the most important parts of the process (Ercanoglu & Gokceoglu
2002). At first, fire occurrences and locations were collected from MODIS (Moderate-Resolution Imaging Spectro Radiometer) satellite images (collected in year

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H.R. Pourghasemi et al.

Figure 2. Flow chart of used methodology in the study area.

2010), extensive field surveys, and national reports. Forest fires are related to year
2010, at 1:25,000 scale (table 1). Out of 151 forest fire locations, 70% were used in the
model training and the remaining 30% were used for validation (Pourghasemi et al.
2012a; Zare et al. 2012; Pourghasemi et al. 2014; Regmi et al. 2014). For FFSM in
the study area, seven effective factors were considered. These factors include altitude,
slope aspect, slope angle, annual temperature, wind effect, land use, and normalized
different vegetation index (NDVI). The spatial database for the study area is shown
in table 1.
Table 1. Data used for forest fire susceptibility mapping (FFSM).
Data layers

Data format

Forest fire locations map

Point

Topographic map

Line and point

Land use

Polygon

Normalized difference
vegetation index (NDVI)
Meteorological data

Grid
Excel data

Source of data
Satellite image, aerial photos,
and extensive field surveys
National Cartographic
Center (NCC)
National Geographic
Organization (NGO)
National Geographic
Organization (NGO)
Iranian Meteorological
Organization (IRIMO)

Scale
1:25,000
1:50,000
1:100,000
30m £ 30m
À

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One of the important factors in any fire hazard rating system is topography data.
In the literatures, the impacts of elevation, slope aspect, and slope angle in fire behaviour have been widely reported (Chuvieco & Congalton 1989; Erten et al. 2004;
Renard et al. 2012; Adab et al. 2013).
In the current research, a digital elevation model (DEM) was created by digitizing
contours (30 m interval) and survey base points. The DEM map has a grid size of
30 m with 2323 columns and 1657 rows. Using the DEM, altitude, slope aspect, and
slope angle were extracted (figures 3(a) and (b)).
Elevation is a crucial physiographic variable that is associated with temperature,
moisture, and wind (Xiangwei et al. 2011). Therefore, it has an important role in fire
spreading (Jaiswal et al. 2002). The altitude map was extracted from the DEM and
classified into five classes according to equal interval classification (Pradhan et al.
2007; Pourtaghi et al. 2014); that is, (1) <500 m, (2) 500À1000 m, (3) 1000À1500 m,
(4) 1500À2000 m and (5) >2000 m (figure 3(a)).
Slope aspect is another factor that correlated with the amount of received solar
energy in the area. Therefore, slope aspect layer was selected as one of the forest
fire-related factors and has been categorized into nine classes: (1) flat, (2) north,
(3) north-east, (4) east, (5) south-east, (6) south, (7) south-west, (8) west, and
(9) north-west (figure 3(b)).
Also, one of the parameters that influence the fire spread rate is slope angle (Weise
& Biging 1997). Fire can move more quickly up the slope and less quickly down the
slope (Kushla & Ripple 1997). So, the slope map of the study area is derived from
the DEM and divided into four classes such as 0 À5 , 5 À15 , 15 À30 , and >30
(figure 3(c)). In addition, using the meteorological database, the annual temperature
and wind effect factors were calculated (figures 4(a) and (b)).

Temperature highly affects the moisture amount in forest combustion. High
temperature led to dry combustion quickly (Antoninetti et al. 1993). The annual
temperature map was classified as follows: <15, 15À16, 16À17, 17À18, and
>18  C (figure 4(a)).
Wind is an important factor because it provides fresh oxygen and the flame puts a
new fuel source (Rawat 2003). Wind effect factor map was created based on three
input parameters, such as DEM in grid format, wind direction (degree), and wind
speed (m/s) in SAGA GIS ( In the current research, the wind effect was
prepared in SAGA-GIS and classified based on the natural break classification
scheme (Pourtaghi et al. 2014) into three classes such as (0.75À0.95), (0.95À1.14),
and (>1.14) (figure 4(b)).
The land use map was created using Landsat-7 images of 2010. In order to create
the land use map, a supervised classification using maximum likelihood algorithm
was applied. A total of 370 signatures (training classes) were collected from all land
use types. The signatures were collected by field survey and using GPS. Out of these
370 signatures, 250 signatures were used for land use mapping and the remaining
were used for accuracy assessment. Nine land use classes were drawn such as irrigation farming (IF), dense forest (DF), sparse forest (SF), irrigated and rain-fed mixed
farming (IRMF), rain-fed farming (RF), good range (GR), moderate range (MR),
moderate forest (MF), woodlands and shrubbery (WS), and urban (residential) (U)
(figure 5).
For assessment of vegetation cover, we used normalized difference vegetation
index (NDVI), which is the most commonly used index to assess live FMC (Chuvieco


H.R. Pourghasemi et al.

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866

Figure 3. Topographical parameter maps of the study area: (a) altitude, (b) slope aspect and
(c) slope angle. Modified from Pourtaghi et al. (2014).


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867

Figure 3. (Continued)

2003). The NDVI was prepared using Landsat-7 images (path 162 and row 34
obtained on 13 November 2010) based on the following equation (Rouse et al. 1973):
NDVI ¼

NIR ¡ RED
;
NIR þ RED

(1)

where NIR (band 4) and RED (band 3) values are the infrared and red portion of the
electromagnetic spectrum, respectively. In this study, the NDVI map was prepared in
ENVI 4.8 and divided into six classes (figure 6).
For classification of conditioning factors, different methods were used such as
equal interval, natural break, and normal or common standards. Finally, for application of M-AHP and MFL models, all the aforementioned conditioning factors were
converted to a raster grid with 30 m £ 30 m pixel size in the ArcGIS 9.3 software. All
the maps are in UTM (Universal Transvers Mercator) coordinate system and
WGS84 spatial reference (WGS84-UTM-Zone40N).

3.2. Statistical index
In this research, the statistical index (SI) model was applied to illustrate the quantitative relationship between distributions of forest fire occurrences with predictor factors. The SI method is a bivariate statistical analysis proposed by van Westen (1997).
A weight value for each categorical unit is defined as the natural logarithm of the


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868

Figure 4. Meteorological parameter maps of the study area: (a) annual temperature and
(b) wind effect (no dimension). Modified from Pourtaghi et al. (2014).


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Figure 5. Land use map of the study area. Modified from Pourtaghi et al. (2014).

forest fire density in the categorical unit divided by the forest fire density in the entire
map (van Westen 1997; Rautela & Lakhera 2000; Cevik & Topal 2003; Pourghasemi,
Moradi, et al. 2013). This method is based on the following equation (van Westen
1997):

WSI ¼ ln


Fij =FT
;
Pij =PT

(2)

where WSI is the weight given to a certain class i of parameter j; Fij is the number of
forest fires in a certain class i of parameter j; FT is the total number of forest fires in
the entire map; Pij is the number of pixels in a certain class i of parameter j; and PT
is the total pixels of the entire map.
3.3. Modified analytical hierarchy process
The analytical hierarchy process (AHP) is a theory of measurement for considering
tangible and intangible criteria that have been applied to numerous areas, such as
decision theory and conflict resolution (Vargas 1990; Yalcin 2008; Youssef et al.
2011). The AHP includes a matrix-based pairwise comparison of the contribution
of different factors on forest fire occurrence. The process consists of four phases:


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H.R. Pourghasemi et al.

Figure 6. Normalized different vegetation index (NDVI) map of the study area. Modified
from Pourtaghi et al. (2014).

(1) breaking a complex unstructured problem down into its component factors, i.e. conditioning factors considered in this study; (2) combine these factors in a hierarchical
order; (3) assign numerical values according to the relative important of each factor

(pairwise comparison); and (4) synthesize the rating to determine the priorities to be
assigned to these factors (Saaty & Vargas 2001). The pairwise comparison is the process
of comparing the relative importance, preference, or likelihood of two elements (for
example, criteria) with respect to another element (for example, the goal) in the level
above to establish priorities for the elements being compared (Saaty 1994; table 2).
One of the key points in AHP is calculation of consistency ratio (Saaty 1977). If
consistency ratio is less of 0.1, then the mentioned matrix can be considered as an
acceptable consistency (Saaty 1977). Several researchers have used AHP model in
various applications and reported a reasonable accuracy (Ayalew et al. 2005; Hajeeh
& Al-Othman 2005; Komac 2006; Yalcin 2008; Esmali Ouri & Amirian 2009; Wu &
Chen 2009; Langenbrunner et al. 2010; Abba et al. 2013; Agarwal et al. 2013;
Kayastha et al. 2013; Tierno et al. 2013; Zhang et al. 2013; Giri & Nejadhashemi
2014). However, it is based on expert opinions and thus may be subjected to cognitive limitations with uncertainty and subjectivity (Pourghasemi, Moradi, et al. 2013).
Thus, for solving the limitations of the conventional AHP, an M-AHP is proposed
by Nefeslioglu et al. (2013) and applied in the current research. The differences
between the M-AHP and the conventional AHP can be classified into two groups:


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Table 2. The AHP pairwise comparison scale (Saaty 1994).
Numerical values
1
3
5
7
9

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2, 4, 6, and 8

Verbal scale
Equal importance of both
elements
Moderate importance of one
element over another
Strong importance of one element
over another
Very strong importance of one
element over another
Extreme importance of one
element over another
Intermediate values

Explanation
Two elements contribute equally
Experience and judgment favour
one element over another
An element is strongly favoured
An element is very strongly
dominant
An element is favoured by at least
an order of magnitude
Used to compromise between two
judgments

(1) The preparation of the factor comparison matrix. In this step, there are following differences:

 The factor comparison matrix in the M-AHP model is not according to the
expert opinion,
 The expert viewpoints in M-AHP only used to define the maximum scores
for each factor in order to prepare factor score matrix,
 Normalization of the factor score values according to maximum score
factor,
 Finally, construction of factor comparison matrix based on modified
importance value scale (Nefeslioglu et al. 2013).
(2) The evaluation of the importance distributions of the conditioning factors on
the decision points (Nefeslioglu et al. 2013). In this step, at first, each factor
will be normalized based on its own maximum score. Subsequently, linear distance between the normalized factor score and decision points will be calculated, and finally decision point comparison matrix will be prepared by
considering modified importance value scale.
In other words, it is sufficient for the expert to identify important factors at model
running ( The
details of the mentioned algorithm/tool (M-AHP) with an example on snow avalanche
can be found in Nefeslioglu et al. (2013).

3.4. Mamdani fuzzy logic
The fuzzy set theory was first introduced by Zadeh (1965), and it is one of the tools
used to handle complex problems. Fuzzy sets theory is a mathematical method used
to characterize and propagate uncertainty and imprecision in data and functional
relationships (Kurtener & Badenko 2000). The mentioned theory has been commonly used in different scientific studies and disciplines (Juang et al. 1992; Alvarez
Grima & Babuska 1999; Ercanoglu & Gokceoglu 2002; Nefeslioglu et al. 2006;
Saboya et al. 2006; Gokceoglu et al. 2009; Yagiz & Gokceoglu 2010; Akgun et al.
2012; Pourghasemi et al. 2012b; Osna et al. 2014). In the fuzzy set theory,


872

H.R. Pourghasemi et al.

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membership can take on any value between 0 and 1, reflecting the degree of certainty
of membership (Zadeh 1965; Pradhan 2011). A membership value was chosen arbitrary according to subjective judgement about the relative importance of the maps
and their various states (Bonham-Carter 1994). A number of different types of membership functions (MFs) have been proposed for fuzzy inference system. These MFs
are triangular, trapezoidal, sigmoidal, bell, Gaussian combination, and p-shaped
(Pradhan 2013; Osna et al. 2014).
Meanwhile, in the literature, different fuzzy inference systems (FIS) have been proposed (Mamdani, Sugeno, and Tsukamoto), but the Mamdani fuzzy is one of the
most interesting methods applied in engineering geology problems (Alvarez Grima
2000; Akgun et al. 2012).
In the Mamdani fuzzy model, ifÀthen rules replace the usual set of equations used
to characterize a system (Yager & Filev 1994). The Mamdani fuzzy model takes the
following form:
Ri :

if x1 is Ai1 . . . and xj is Aij ; then y is Bi
for i ¼ 1; 2; :::; k and j ¼ 1; 2; :::; r;

(3)

where k is the number of rules, xj ðj ¼ 1; 2; . . . ; rÞ are input variables, y is the output
variables, and Aij and Bi are linguistic terms.
In Mamdani model, each rule is a fuzzy relation Ri ðX £Y ! ½0; 1ŠÞ which is calculated using the following equation:
mRi ðx; yÞ ¼ IðmAi ðxÞ; mBi ðyÞÞ;

(4)

where the operator I can be either a fuzzy implication or a conjunction operator (tnorm) (Jager 1995).
There are four inference steps in Mamdani fuzzy inference system such as fuzzification, rule assessment, aggregation, and defuzzification steps (Mamdani & Assilian

1975); they are presented in equations (5)À(7).
Step 1: compute the degree of fulfillment ai of the antecedent for each rule i:
ai ¼ mAi1 ðx1 Þ ^ mAi2 ðx2 Þ ^ ::: ^ mAin ðxn Þ:::1  i  k:

(5)

0

Step 2: for each rule, drive the output fuzzy set Bi using the minimum t-norm:
mB0 ðyÞ ¼ ai ^ mBi ðyÞ:
i

(6)

Step 3: Aggregate the output fuzzy sets by taking the maximum method (Eq. 8):
mB 0 ¼

maxm

i D 1;2;:::;k

0


:

¡iðyÞ
B

(7)

Finally, in this study, due to defuzzification process (Step 4) was used of the centroid method because of its simplicity and producing consistent results (Jager 1995).
The details of the mentioned algorithm can be found in Alvarez Grima (2000).


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873

As a result, the fuzzy logic allows more flexible combinations of weighted maps
and could be readily implemented in GIS modelling language (Pradhan 2010a,
2010b).
For applying of the mentioned models (M-AHP and MFL) were used of a laptop
computer by a hardware configuration such as CORE i7 processor, 64-bit CPU and
6GB RAM, the inference process completes in approximately 7 hours. But the computer output in the M-AHP model can be obtained in a less time, almost at 10 min.
4. Results

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4.1. Statistical index (SI)
The spatial relationship between forest fire occurrences and the conditioning factors
using the SI model is presented in table 3. According to the relationship between forest fire occurrence and altitude, forest fire numbers or frequency is highest at elevations higher than 500 m. Thus, the probability of occurrence of fire in these altitudes
is higher. Chuvieco and Congalton (1989) and Adab et al. (2013) reported that fire
behaviour trends are less severe at higher altitudes because of higher rainfall. The SI
value between forest fire occurrence and slope aspect shows that forest fires are most
common on east (SI D 0.89), south-west (SI D 0.38), and west (SI D 0.21) facing
slopes, respectively, and the flat (SI D ¡0.5) and north-west (SI D ¡0.36) facing
slopes have the lowest incidence. The analysis of SI for the relationship between forest fire occurrence and slope degree indicates that slope degree class >30 and <5
has the highest and lowest values of SI (0.64 and ¡0.75), respectively. The results
showed that the SI values decreased with the slope degree addition in the study area.

Assessment of annual temperature showed that the temperature of 16À17  C has
high correlation with forest fire occurrence (SI D 0.55). The result of wind effect indicates that class >1.14 is considered to be susceptible to fire with an SI value of 0.47.
In the case of land use, it can be inferred that 99.06% of forest fire falls on dense forest area with value of 0.51, indicating that the probability of occurrence of forest fire
in this land use type is very high. The NDVI factor indicates that the range between
0.1 and 0.5 and >0.5 have the highest frequency for forest fire occurrence (26.42%
and 72.64%). On the other hand, the remaining classes are lowest susceptible or nonsusceptible to forest fire occurrence.
4.2. Forest fire susceptibility mapping (FFSM) by M-AHP model
For FFSM, a tool was developed in Netcad GIS 6 as M-AHP. At first, seven forest
fire conditioning factors were classified based on literature review and expert knowledge. Then, the score of their classes (condition factor classes) was determined
according to expert knowledge (table 4) and, subsequently, maximum score factors
were extracted. In the mentioned table, for the altitude, the maximum score is 7 for
classes >2000 m. Hernandez-Leal et al. (2006) reported that humidity and temperature have higher influence on fire at higher altitude areas than lower ones. In the case
of slope aspect, maximum scores referred to south-facing slopes by value of 6. It is
noticed that south-facing slopes received more sunlight, higher temperatures, stronger winds, low humidity, and low fuel moistures than those facing the north pole.
Thus, vegetation is generally drier and less dense on south-facing slopes than northfacing ones (Anderson 1982; Prasad et al. 2008), and drier fuels are more prone to


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H.R. Pourghasemi et al.

Table 3. Spatial relationship between conditioning factors and forest fire locations using
statistical index (SI) model.

Factor
Altitude (m)

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Slope aspect

Slope angle (degree)

Annual
temperature (mm)

Wind effect

Land useÃ

NDVI

Class

No. of
pixels in
domain (Pij)

% Pixels
in
domain

No. of
forest
fires (FFij)

% forest
fires

SI

<500
500À1000
1000À1500
1500À2000
<2500
Flat
North
North-east
East
South-east
South
South-west
West
North-west
<5
5À15
15À30
>30
<15
15À16
16À17
17À18
>18
0.75À0.95
0.95À1.14
>1.14
IR
DF
LF

IRMF
RF
GR
MR
MF
WS
U
<¡0.001
¡0.001 to 0.00
0.00À0.05
0.05À0.1
0.1À0.5
>0.5

464,718
534,642
521,829
169,287
10,809
316,740
209,679
139,591
92,104
100,549
134,018
143,326
195,660
369,618
544,326
400,683

621,420
134,856
40,017
393,766
647,991
485,269
134,242
721,245
629,205
350,835
199,564
1,012,790
22,381
95,828
347,307
267
1345
12,649
634
8520
453
302
1527
7280
1,005,442
686,281

27.32
31.43
30.67

9.95
0.64
18.62
12.32
8.21
5.41
5.91
7.88
8.43
11.50
21.73
32.00
23.55
36.53
7.93
2.35
23.15
38.09
28.52
7.89
42.39
36.98
20.62
11.73
59.53
1.32
5.63
20.41
0.02
0.08

0.74
0.04
0.50
0.03
0.02
0.09
0.43
59.10
40.34

6
44
43
11
2
12
15
9
14
5
7
13
15
16
16
25
49
16
1
13

70
22
0
41
30
35
0
105
1
0
0
0
0
0
0
0
0
0
0
1
28
77

5.66
41.51
40.57
10.38
1.89
11.32
14.15

8.49
13.21
4.72
6.60
12.26
14.15
15.09
15.09
23.58
46.23
15.09
0.94
12.26
66.04
20.75
0.00
38.68
28.3
33.02
0
99.06
0.94
0
0
0
0
0
0
0
0

0
0
0.94
26.42
72.64

¡1.57
0.28
0.28
0.04
1.08
¡0.50
0.14
0.03
0.89
¡0.22
¡0.18
0.38
0.21
¡0.36
¡0.75
0.001
0.24
0.64
¡0.92
¡0.64
0.55
¡0.32
0
¡0.09

¡0.27
0.47
0
0.51
¡0.34
0
0
0
0
0
0
0
0
0
0
0.78
¡0.81
0.59

Ã
IF D irrigation farming, DF D dense forest, LF D low forest, IRMF D irrigated and rain-fed mixed farming,
RF D rain-fed farming, GR D good range, MR D moderate range, MF D moderate forest, WS D woodlands
and shrubbery, U D urban (residential).


Geomatics, Natural Hazards and Risk

875

Table 4. The conditioning factor scores given by expert and maximum scores.

Factor
Altitude (m)

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Slope aspect

Slope angle (degree)

Annual temperature ( C)

Wind effect

Land use

NDVI

Class

Score

Maximum score

<500
500À1000
1000À1500
1500À2000
>2000
Flat
North

North-east
East
South-east
South
South-west
West
North-west
0À5
5À15
15À30
>30
<15
15À16
16À17
17À18
>18
0.75À0.95
0.95À1.14
>1.14

1
2
3
5
7
1
2
2
3
4

6
5
4
3
1
3
5
7
1
2
3
5
7
1
3
5

7

IR
DF
LF
IRMF
RF
GR
MF
WS
U
IR
<¡0.001

¡0.001À0.00
0.00À0.05
0.05À0.1
0.1À0.5
>0.5

1
5
3
1
1
1
1
1
1
1
1
1
1
3
4
7

6

7

7

5

5

7


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876

H.R. Pourghasemi et al.

ignition (Noonan 2003; Iwan et al. 2004). For the slope degree, the maximum score is
7 for slopes >30 . Kushla and Ripple (1997) stated that fire can move more quickly
up the slope and less quickly down the slope. On the other hand, flames being angled
closer to the ground surface, thus, fire spread rate may rise on steeper slopes, and the
process of heat convection can be enhanced by wind effects due to fire behaviour
(Whelan 1995; DeBano et al. 1998; Adab et al. 2013). The maximum score for annual
temperature showed that higher temperature caused dry combustion (Artsybashev
1983; Antoninetti et al. 1993) and the resulting classes >18  C have the highest maximum value (score D 7). In the case of wind effect and according to the expert knowledge scores, when wind effect increases, the probability of forest fire increases. In
other words, the rate of burning is increased with increasing of wind effect. (Rawat
2003). So, classes of >1.14 have a highest score (score D 5). Another important factor
for forest fire occurrence is land use type. The relationships between forest fire locations and land use types show that 99.06% of forest fire falls on dense forest area
with value of 0.51, indicating that the probability of occurrence of forest fire in this
land use type is very high. Thus, for M-AHP model, the maximum score (score D 5)
corresponded to dense forest land use type. In the case of NDVI, maximum score
was 7. In the range of greater than 0.5, the study area is generally covered by dense
vegetation and tropical rainforest, so it is susceptible to fire occurrence.
Another important problem in calculation of M-AHP is instant factor scores. During application of the model, for any terrain mapping unit in the field, instant factor
scores should also be defined by the expert or relevant user (Nefeslioglu et al. 2013).

But, one of the attributes of Netcad GIS 6 is that the instant scores will be obtained
from the region basin (study area basin) automatically. In other words, they are computed from the raster maps used in this study. For running the M-AHP model, one
key point is determination of decision points. In the current research, the decision
points for the FFSM is grouped into four classes as low, moderate, high, and very
high (figure 7(a)).
Finally, the FFSM prepared from the M-AHP method, which covered 29.66% of
the total area, was designated to be a low FFSM class; 20.78, 33.17, and 16.39% of
the total area are related to moderate, high, and very high FFSM zones, respectively
(figure 7(a)).

4.3. Forest fire susceptibility mapping (FFSM) by Mamdani fuzzy logic
To produce FFSM using Mamdani fuzzy inference system (FIS), at first, conditioning factors were created in raster format with a 30 m £ 30 m pixel size in GIS environment. After the inputs were loaded to Netcad GIS 6, the user can select a fuzzy
existing model or build a new one based on the input numbers in the Architect page.
In the next step, membership functions were defined for input and output layers. The
input layers are the same as considered in the previous models.
Due to definition of membership function for categorical factors such as slope
aspect and land use, the forest fire density and spatial relationship between these factors and forest fire locations (table 1) were considered in the current research. To
minimize the uncertainty, a 50% overlap is applied between the fuzzy sets for each
input parameter, and triangular membership functions are used for each fuzzy set
(Akgun et al. 2012). On the other hand, output set as FFSM and its membership
function draw into four classes such as low, moderate, high, and very high. Another


877

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Geomatics, Natural Hazards and Risk

Figure 7. Forest fire susceptibility map (FFSM) produced by M-AHP (a) and MFL

(b) models. Modified from Pourtaghi et al. (2014).


H.R. Pourghasemi et al.

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878

Figure 8. Prediction rate curve for the forest fire susceptibility map by MFL (a) and M-AHP
(b) models.


Geomatics, Natural Hazards and Risk

879

important part in Mamdani FIS is definition of the fuzzy ifÀthen rules. In this study,
a total 128 ifÀthen rules were used. First, the ifÀthen rules are created automatically
and then edited according to expert opinion. For transforming output linguistic variables into crisp values, defuzzification method should be used. Thus, for FFSM, the
most common defuzzification method, namely, gravity center, was used (Jager 1995;
Babuska 1996; Saboya et al. 2006).
Finally, the FFSM produced from the MFL method, which covered 23.17% of the
total area, was designated to be a low FFSM class; 24.41, 38.97, and 13.45% of the
total area are related to moderate, high, and very high FFSM zones, respectively
(figure 7(b)).

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4.4. Validation of forest fire susceptibility maps

In general, the validation of predicted results is the crucial step in the modelling process, so that the results can provide a meaningful interpretation (Fabbri & Chung
2001; Chung & Fabbri 2003). To determine the accuracy of the two FFSMs produced using M-AHP and MFL models, the ROC curve (Pradhan et al. 2009;
Pradhan 2010a, 2010b, 2011; Akgun et al. 2012; Mohammady et al. 2012; Pourghasemi,
Goli Jirandeh, et al. 2013; Osna et al. 2014; Jaafari et al. 2014) was used. ROC curve
analysis is a common method used to assess the diagnostic test accuracy (Egan
1975). The ROC curve plots the true positive rate on the Y-axis and the false positive
rate on the X-axis. It represents the trade-off between the two rates (Negnevitsky
2002). In the ROC method, the area under the curve (AUC) values range from 0.5 to
1.0 and are used to evaluate the model accuracy (Nandi & Shakoor 2010). If the
model does not predict the forest fire occurrences better than the chance, the AUC
would equal 0.5. A ROC curve of 1 shows perfect prediction (Yesilnacar 2005). The
quantitativeÀqualitative relationship between AUC and prediction accuracy can be
classified as follows: 0.9À1, excellent; 0.8À0.9, very good; 0.7À0.8, good; 0.6À0.7,
average; and 0.5À0.6, poor (Yesilnacar 2005). In this study, the forest fire locations
that were not used during the model building process were used to verify the FFSMs.
The AUC values of the ROC curve for MFL and M-AHP models were found to be
88.20% and 77.72% with a standard error of 0.042 and 0.49, respectively (figures 8
(a) and (b)). Hence, it is concluded that the MFL model employed in this study
showed more reasonable results with respect to the M-AHP model in predicting the
forest fire susceptibility of study area.

5. Conclusion
The present study attempts to assess FFSMs produced by the M-AHP and MFL
models and to compare their performances. The results of this study suggest that
FFSMs for the Minudasht Township, Golestan Province, of Iran are viable. Meanwhile, based on the obtained AUC, the MFL model has better prediction performance (88.20%) than the M-AHP (77.72%) model. As a result, the FFSM by MAHP is easy, because the mentioned model does not use the membership functions
and ifÀthen fuzzy rules; thus, it takes a less time for model running. According to
Nefeslioglu et al. (2013), another advantage of the M-AHP is that, using this methodology, the consistency ratio value for the comparison matrix and the weights never
exceeds 0.10. In contrast, fuzzy algorithm is a powerful tool in modelling of complex

880

H.R. Pourghasemi et al.

natural processes and nonlinear systems. Finally, these maps can be used to early
warning, fire suppression resources planning, and allocation works.
Acknowledgements
The authors would like to thank Prof. Candan Gokceoglu, Dr Hakan A. Nefeslioglu, Turgay
Osna, and Deniz Celik for their supports in creating and training of spatial analyst in the Netcad GIS 6, especially on M-AHP and MFL modules. Also, the authors would like to thank
the three anonymous reviewers for their helpful comments on the previous version of the
manuscript.

ORCID

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Biswajeet Pradhan

/>
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