International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004
The criterion weights were calculated as 0.26, 0.21, 0.17,
0.16, 0.10, 0.06, and 0.04 respectively for annual rainfall, size
of watershed, basin slope, gradient of main drainage channel,
drainage density, land use and type of the soil. With the input
values in pairwise comparison and weights calculated,
consistency ratio (CR) was found as 0,042. This indicated a
reasonable level of consistency in the pairwise comparison of
the factors.
Three composite maps showing the flood vulnerable areas
were created using multicriteria evaluation methods with
GIS, namely Boolean Method and two WLC Methods are
presented in Figures 3.3 - 3.5. The user interface was
designed such that a decision maker could step through a
weighting process with pairwise comparison that would result
in the calculation.
£ BOOLEAN METHOD
ww
—
Figure 3.3: The final map that is created with Boolean
Method.
X: RANKING METHOD 10x
+f RANKING =
f
M i
-
Figure 3.4: The final map that is created with Ranking
Method.
LM C crai er aua [-[n[x]
| PAIRWISE Le)
HISH i
m MEDIU M-HIGH|
[J MEDIUM |
LOW-MED IUM |
ow |
Ca No D ata i
hd
This interface and the processes were presented in Figure 3.6,
It involves: Viewing all criteria in a list box and each criteria
pair in a matrix, Entering his or her preferences and ranks for
cach criteria pair, Calculating weights from the input ranks
and preferences with Visual Basic Application VBA program
embedded in GIS environment, Calculating consistency ratio
on weights and input ranks, Modifving the ranks or
preferences according to the user's choice.
Factors
Precipitation
Size of Watershed
Default Factors |
EE EEN ER ER EE
BFE Ess [5 E
i ENTE
Figure 3.6: All calculations with weights and consistency
ratio.
3.4 Fuzzy Measure Application in MCE
Zheng and Kainz (1999) have stated that uncertainty is
endemic in GIS and it is best to draw attention to it because
of its complexity and potentially damaging effects on
decision making. The OWA decision rule is based on the
principles of the fuzzy set aggregation (Malezewski. 1999a;
Malczewski, 1999b). In GIS and decision making context,
vulnerability was considered as a fuzzy concept expressed as
a fuzzy set membership. Jiang and Eastman (2000) presented
an example paper about suitability as a fuzzy concept. To
rescale the range to a common numerical basis is unclear and
involves fuzziness. The criteria were standardized to a
common numeric range and then combined by weighted
averaging. In WCL, criterion weights and ordered weights
were used. Finally the illustrations gave an evaluation of
vulnerability of flood hazard. The result of OWA method for
MAX showed the most vulnerable areas and MIN showed the
least vulnerable areas. AVERAGE fell midway between two
extreme cases of fuzzy MIN and MAX operation and had a
full tradeoff. Many other solutions were possible. The
solutions were the effects of the distribution of risk and
tradeoff. With this method, the aim was not to ignore the poor
qualities, but they could be compensated for. Different
illustrations give important idea to planners for location
solutions. By compensating one criterion, decision maker can
create different solutions.
3.5 Sensitivity Analysis
Sensitivity analysis depends on the error in the input data as
criterion weights and criterion attributes. The sensitivity
between the criteria basin slope and main channel gradient
was analyzed. With addition of small perturbations to the
weights for 10 cells, the ranking has changed for only one
cell. This result indicates that those two criteria are not so
sensitive for this analysis. When the sensitivity of criterion
values was analyzed, it was also seen that there were no
significant changes in the ranking with small perturbations on
the gradient of the main channel criterion.
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