International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004
Corcoran et al. (1997) identified the optimal areas of location
for the development of livestock enterprises within the
European Union based on physical, climatic and socio-
economic characteristics for areas being evaluated. Biermann
(1998) presented the land suitability assessment methodology
with multi-disciplinary data from different sources. The most
appropriate location for low-income residential development
was identified by integrating a wide range of data.
Environmental or natural resources management decisions
required the analysis of spatial information. GIS technologies
had ‘been used to facilitate decision making in many different
fields. Tkach and Simonovic (1997) applied Compromise
Programming technique within a GIS in order to cvaluate
floodplain for Red River Valley region in Manitoba, Canada.
Antonie et al. (1997) presented an example application on
integration of multicriteria evaluation technique with GIS for
sustainable land use in Kenya; maximizing revenues from
crop and livestock production, maximizing food output,
maximizing district self-reliance in agricultural production,
minimizing environmental damages from erosion. Thomas
(2002) aimed to make brownfield sites competitive with
undeveloped sites and returned these areas to productive uses
by evaluating land use options with respect to brownfields
inventory, characterization, and potential for redevelopment.
Duijm and Markert (2002) searched the environmental
impact and safety aspects for alternative scenarios for
disposing of ammunition.
The main aim of this study is to generate a composite map for
decision makers by using some effective factors causing
flood. The study reviewed the role of GIS in decision-making
and then outlined the evaluation approach for many criteria in
decision process. The design of multicriteria environment
attempted to use a variety of evaluation techniques to data
from GIS and presented them in a manner familiar to
decision makers. By integrating the evaluation techniques
with GIS, it was intended that the effective factors would be
evaluated more flexibly and thus more accurate decision
would be made in a shorter time by the decision makers. By
evaluating the criteria, the values of the criteria were
classified to explain the opinions and preferences. Boolean
and WLC approach were used in integrating MCE with GIS.
The uncertain knowledge in multicriteria decision making
was held by considering standardized criteria as fuzzy
measures, where fuzzy set theory was emphasized. Different
weights were given to the citeria in fuzzy extent with the
Ordered Weighted Averaging (OWA) method. Finally all the
composite maps created with these approaches were
compared with the flooded area obtained from a hydraulic
model.
2. METHODS
2.1 The General Outline
The flood vulnerability analysis applied in this study consists
of two basic phases. Firstly, the effective factors causing
floods are determined. Secondly several approaches to MCE
in a GIS environment are applied and these approaches are
evaluated in finding the flood vulnerable areas.
The evaluation procedure consists of the following steps:
|. The assessment of a vulnerability structure: choosing the
effective factors and determining their importance and
how they affect the flood vulnerability.
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2. Producing map layers: raw data acquisition and
transferring to appropriate GIS layers.
3. Cartographic modeling: defining the vulnerable areas
using several approaches to MCE.
4. Sensitivity analysis: demonstrating the effect of different
criterion weights on the spatial pattern of the vulnerable
areas.
2.2 Assessment of A Vulnerability Structure
The first step in assessing the vulnerability structure is to
determine the factors affecting the flood on the basis of an
analysis of existing studies and knowledge. Here, judgments
made by experts on hydrology and hydraulics can be applied.
These factors are used as criterion separately. A criterion is a
basis for a decision that can be measured and evaluated
(Eastman et al., 1995). Layers representing the criteria are
referred to as criterion maps.
2.3 Producing Map Layers
A GIS application is used for managing, producing,
analyzing and combining spatial data. The data needed in this
study are produced from collected or existing data by using
different kinds of spatial functions and analysis.
2.4 Cartographic Modeling
Cartographic modeling is applied in producing and
combining spatial data describing the causing factors. In the
first phase, the vulnerable areas are produced by numerically
overlaying a map layer describing the study area. This
overlay is carried out as a Boolean overlay.
In the second phase ranking method is used. In Ranking
Method, every criterion under consideration is ranked in the
order of the decision maker's preference. To generate
criterion values for each evaluation unit, each factor was
weighted according to the estimated significance for causing
flooding. The inverse ranking was applied to these factors. |
is the least important and 8 is the most important factor as in
Pramojanee et al. (2001). The criteria with their raw data
were typically noncommensurate. To make the various
criterion maps comparable, a standardization of the raw data
was usually required (Malczewski, 1999a; Lin et al., 1997;
Jiang and Eastman, 2000; Eastman et al., 1995). Linear Scale
Transformation was adopted as a standardization procedure,
because it is the most frequently used method for
transforming the input data into commensurate scale. 0 is the
worst-standardized score and 1 is the best-standardized score
(Malezweski, 1999a) Through standardization, criterion
Scores were expressed according to a consistent numeric
range, 0 and 1000, by multiplying with the constant number
1000. In fact, the aim was to get the range between 0 and 1,
but GIS program accepted the calculation as only 0 and 1 as
if it was a True/False evaluation. At the end of the
standardization process, each factor had an equivalent
measurement basis before any weights were applied.
In the third phase Pairwise Comparison Method is used in
determining the weights for the criteria. This method involves
the comparison of the criteria and allows the comparison of
only two criteria at once. This method can convert subjective
assessments of relative importance into a linear set of weights
(Heywood et al., 1993). It was developed by Saaty (1980) in
the context of a decision making process known as the
Analytical Hierarchy Process (AHP) (Malczewski, 1999a;
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