IAPRS & SIS, Vol.34, Part 7, “Resource and Environmental Monitoring”, Hyderabad, India, 2002
Hierarchy Process (AHP). The initial step in the AHP method is
to form a hierarchy of objectives, criteria and other elements
involved in a problem. The site selection for water harvesting
structure problem was structured in the form of five-level
hierarchy. The hierarchical structure represents the objective/
goal, opinions of the decision-makers, parameters, dependency
among the parameters and categories within the parameters.
Once the hierarchical structure has been formed and
comparison matrices were developed (which are evaluated) by
the decision makers on the intensity of difference in
importance, expressed as a rank number on a given numerical
scale for each level in the hierarchy and to construct a scale of
importance and weights or priorities are determined. Based on
this hierarchical structure decision-makers evaluate the
components of each level by pair wise comparison using the 9
Point scale method developed by Thomas.L Saaty & Luis G.
Vargas on Logic of priorities, International series in
Management science/operations Research. An expert is asked
to make pair wise comparisons between two parameters at a
time, to decide which factor is more important form the
weightage point of view, then specify the degree of importance
for that parameter. (Table-3) between 1 and 9, in which 9 is
most important weight associated with it. These evaluations
resulted in reciprocal matrices of the components of each level
against the items in the level above. Consistency check was
made and it was found that consistency ratio does not exceed a
value of 0.2. Normalization of Eigen vector corresponding to
maximum Eigen value was done. This gives priorities for
parameters and categories within parameters.
Table-3 intensity of importance (weightage) scale
Intensity of importance Definition
1. Equal importance
3. Weak importance
5.Essential or strong importance
7.Demonstrated importance
9. Absolute importance
2,4,6,8 are Intermediate values between the two adjacent
Judgments when compromise needed
Reciprocals of » non-zero: If activity i has one of the above
non-zero numbers assigned to it numbers when Compared With
activity J, then J has reciprocal value when compared with it.
1) The decision-makers are the experts in the fields of
Geomorphology, soils, lithology and hydrology domains and
they were asked rolling queries about the parameters & their
importance in the system to rank various factors shown in the
parameters level of the hierarchy consider them to be
independent. 2) Each decision-maker was asked to rank the
dependency among the parameters. Each one's response would
be used to create the dependence priorities among the
parameters. To establish final priorities of the parameters that
reflect the interdependence among parameters, the dependence
priorities would be laid out with respect to each parameter in a
matrix and each column of vectors would be multiplied by the
prioty of the corresponding parameter obtained as if they are
independent and the rows added. The result is, the final
priorities that would reflect the inter- dependencies among
parameters. 3) The Decision-Makers at the opinion level were
given equal weightage. Again, the interdependency priorities of
the parameters would be laid out with respect to each decision-
maker in a matrix and each column of vectors is multiplied by
the priority of the corresponding decision-maker. These values
are then added across each row resulting in the composite
priority vector of each of the parameters. The composite weight
of each parameter reflects the importance of the parameter
under the three groups of decision-makers. 4) The last level in
the hierarchy i.e. the categories in the parameters were also
‘ranked by the Saaty's Pair wise comparison method. The
composite weight of each parameter was computed and it
reflects the importance of the parameter under the three groups
of decision makers. (Table-4)
Tabled: Parameter weightage for parameters/ Categories
assigned within AHP Method
Parameter Weight Categories (Within Parameter) Weight
Land use 46.484 Built-up area 0.1212
Agricultural land 0.1869
Forest 0.0919
Land with scrub 0.3034
Land without scrub 0.2412
Barren/rocky 0.0389
; Water 0.0166
Geology | 4.430 Unconsolidated 0.4103
Consolidated rock 0.2873
Semi consolidated 0.3023
Soil texture 1.000 Coarse 0.5547
' Moderate 0.3832
Fine 0.0628
Soil depth 19.97 Shallow 0.4834
Medium 0.2181
Deep 0.2985
Rainfall 76.72 High 0.3149
Medium 0.5421
Low 0.1283
Weathered 22.22
Zone thickness High 0.3949
Moderate 0.5422
Less 0.0628
Lineament 100 Present 100.0000
Absent 0.0000
Ground 72.08
Water Excellent 0.1680
Good 0.1838
Moderate 0.4443
Poor 0.2039
Landforms 63.71
Flood plain 0.0646
Structural hill 0.0422
Denudational slope 0.0324
Residual hill 0.0193
Valley fills 0.2953
Pediment 0.0878
Buried pediment 0.2546
Slope 91.702 Check dam <5%-100 >5%-0
Percolation tank<3%-100>3%-0
> Data Integration: Categories within each data layer were
manipulated with the weights obtained in the earlier step and
grids were generated. A grid cell with a size of 50X 50 m was
considered. Weighted linear adaptive model is the one widely
used for data integration. But the weighted linear additive model
has got a major disadvantage. In this, a total compensation
between criteria is assumed and it means that a decrease in one
unit of criteria would be totally compensated by an equivalent
gain on other criteria. Ideal point analysis, a compromise
programming technique has been used here. It is a method to
arrive at non-compensatory solution and has been used for data
integration. It measures the deviations from the ideal point in
each data layer and a min-max rule is applied wherein minimum
of the maximum weighted deviations are sought for getting a
composite layer. The best compromise solution is defined as
that which is the minimum distance from the theoretical ideal.
» Suitability ratings: The composite suitability ratings obtained
through compromise programming was further grouped into
suitability classes like first best, second best, third best. ...Nth
best etc., Statistical techniques like mean, standard deviation
measures have been used for grouping into 5 suitability
classes. Two separate suitability layers were obtained for
Percolation tanks and check dams, since the slope criteria for
both of them were different.
> Elimination of unwanted data: Existing roads, settlements and
water bodies were eliminated from site suitability map.
» Stream ordering: Stream ordering was done and streams
with <3 rd order were selected to identify suitable sites for
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