23 A map represents some evaluation criteria such as S1, S2, S3, and N class. These
ordinal values are used in marine suitability analysis, therefore the classes have to be rated, for example class S1 with respect to a particular criteria to contribute to
the goal. This process is relatively important and called criteria standardization. Commonly evaluation of criteria standardization is described normally on the
range of 0 to 1, or 0-10 or 0-100 etc. In certain evaluation of marine coastal suitability it can be represented by GIS layer and some non spatial data.
In describing levels of the criteria evaluation are required to the weights. In figure 3-3 the criteria weights need to be placed and summed, then the well-established
geometric mean method is used. In this approach all the elements of weight will be multiplied with score until the n
th
. The calculation of these map are divided by their sum to get the normal weights. The hierarchy of the criteria is obtained.
Standardized Criteria maps such as weights are multiplied with these scores at each level of the criteria.
Physical parameter data of depth, salinity, temperature, brightness, dissolve oxygen, and pH are given weighting and index score according to the
characteristics of groups of marine coastal suitability data table 2-1, then the data for weighting, index scoring and value of criteria will be multiplied, summed
up and divided by maximum percentage in amount of thematic data. The formula used is as follows:
Scoring average = weight of salinity + weight of depth + weight of brightness + weight of temperature + weight of
dissolved oxygen + weight of pH 100……………………..6 Pramono, 2004
3.3 Fuzzy Method Approach in Marine coastal Suitability
Fuzzy set theory has been adapted for the use in environment applications, such as for GIS based marine coastal suitability. The fuzzy set classification technique
will provide solution to the problem with various constraints with semantic import model Buorrough and McDonnel, 1998. Model function to calculate
24 membership function MF of physical parameter are depicted in figure 2-1
adapted from Burrough and McDonnel, 1998 .
Figure 3-5. Analysis marine coastal suitable with fuzzy method Figure 3-5, the phase spatial data consist of collected data from the field. The
phase weighting and overlay with fuzzy method is indicated by each physical parameter such as depth, salinity, temperature, brightness, etc., and will be
converted into fuzzy number in the range 0 and 1. Equation member function of fuzzy method is used to calculate the process depend on input data from the field.
The overall process of this method is presented in figure 3-6. Table 2-5. Range marine suitability index use as fuzzy method
Modify
LSI Symbol Suitability
Class
1,00 – 0,80 S1
Most suitable 0,79 – 0,60
S2 Suitable
0,59 – 0,40 S3
Less suitable 0,39
N Not suitable
The first phase criteria layer of figure 3-6 presents the physical parameters criteria. In the second phase spline interpolation, the criteria data is converted
using spline interpolation. This process will generate several classifications
25 according to the data. The third phase Overlay process, the same process with
threshold criteria and SAW method, an overlay operation will join all features of attributes of spatial data from a region in map.
Figure 3-6. Process for fuzzy set method Each point will have a membership in every group, which the source is defined as
criteria of class membership. Level of marine coastal suitability can be divided in several classifications such as S1 class most suitable, S2 class suitable, S3
class less suitable, and N class not suitable. The notation in numeric will be design into S1 class the value in range 1 – 0.8, S2 class the value in range 0.79 –
0.60, S3 class the value in range 0.59 – 0.40, and N class will less than 0.39, as describe in Table 2-5.
26
3.4 Data Sources
