Identification of groundwater recharge potential zones using AHP …
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3.3.1 Analytic Hierarchy Process (AHP)
AHP is a mathematical, matrix-based technique, widely used as a decision-making
tool in various complex methods. It helps to capture both subjective and objective
aspects of a decision. Hierarchical structures have been used here to represent a
complex problem and develop local priorities for alternatives based on the judgment
of the user, and then synthesizes the results for the calculation of global priorities
[24].
AHP, as a weight estimating technique, enables decision-makers to derive weights
rather than assign them arbitrarily. AHP maintains complexity and suitable judgment
while allowing both objective and subjective considerations to be incorporated in the
decision-making process [30].
(i.) The dominant objective is specified first as a goal
(ii.) The goal is then broken down step by step into more specific objectives that
can further be broken down into subobjectives:
(iii.) The attributes lie at the tip of the hierarchy.
3.3.2 Applying AHP in Present Study
In the present study, AHP proposed by [24] is applied to determine the weights. The
AHP method used here has four steps of calculations.
• Creating pairwise comparison matrix;
• Finding highest eigenvalue and then right eigenvector for each criterion;
• Normalizing right eigenvector to delineate weight for individual layers;
• Checking for consistency.
3.3.3 Deriving Pairwise Comparison Matrix
The AHP pairwise matrix is developed by putting relevant importance value for each
criterion involved in the present study. The relative importance values are defined
with the Saaty’s 1 to 9 scale, where a score of 1 denotes equal weight between the
two cases, and a score of 9 means the absolute importance of one case compared
to the other one [24]. An unbiased opinion is taken from each of the individuals of
a three-member expert panel. The average of three individual datasets is then used
as the base pairwise comparison matrix. Table 3 represents the pairwise comparison
matrix obtained based on average expert opinion using Saaty’s nine-point importance
scale used to calculate relative weights for the thematic layers.
145
3.3.1 Analytic Hierarchy Process (AHP)
AHP is a mathematical, matrix-based technique, widely used as a decision-making
tool in various complex methods. It helps to capture both subjective and objective
aspects of a decision. Hierarchical structures have been used here to represent a
complex problem and develop local priorities for alternatives based on the judgment
of the user, and then synthesizes the results for the calculation of global priorities
[24].
AHP, as a weight estimating technique, enables decision-makers to derive weights
rather than assign them arbitrarily. AHP maintains complexity and suitable judgment
while allowing both objective and subjective considerations to be incorporated in the
decision-making process [30].
(i.) The dominant objective is specified first as a goal
(ii.) The goal is then broken down step by step into more specific objectives that
can further be broken down into subobjectives:
(iii.) The attributes lie at the tip of the hierarchy.
3.3.2 Applying AHP in Present Study
In the present study, AHP proposed by [24] is applied to determine the weights. The
AHP method used here has four steps of calculations.
• Creating pairwise comparison matrix;
• Finding highest eigenvalue and then right eigenvector for each criterion;
• Normalizing right eigenvector to delineate weight for individual layers;
• Checking for consistency.
3.3.3 Deriving Pairwise Comparison Matrix
The AHP pairwise matrix is developed by putting relevant importance value for each
criterion involved in the present study. The relative importance values are defined
with the Saaty’s 1 to 9 scale, where a score of 1 denotes equal weight between the
two cases, and a score of 9 means the absolute importance of one case compared
to the other one [24]. An unbiased opinion is taken from each of the individuals of
a three-member expert panel. The average of three individual datasets is then used
as the base pairwise comparison matrix. Table 3 represents the pairwise comparison
matrix obtained based on average expert opinion using Saaty’s nine-point importance
scale used to calculate relative weights for the thematic layers.