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Multi-criteria analysis in GIS environment for natural resource development - a case study on gold exploration
Nihar R. Sahoo,
P.Jothimani and G. K. Tripathy
Tata Infotech Ltd, SEEPZ, Mumbai, 400 096
Abstract
GIS-based analysis of spatial data has been
a new specialized process, capable of analyzing complex problem of evaluating
and allocating natural resources for targeting potential areas for mineral
exploration. This paper explains developing a data-driven decision-tree approach
with multi-criteria evaluations in mineral potential mapping at the Hutti-Maski
schist belt. An inference network based spatial data integration has been
attempted which allows for incorporation of uncertainties into a predictive
model. The procedure has produced a posterior probability map identifying
favorable areas for gold exploration
GIS in Natural Resources
Development
Land resource evaluation and allocation is one of the most
fundamental activities of resource development (FAO, 1976). With the advent of
GIS, there is ample of opportunities for a more explicitly reasoned land
evaluation. Prediction of suitable areas for mineral exploration in a virgin
area of specific type are problems that require use of various procedures and
tools for development of decision rule and the predictive modeling of expected
outcomes. GIS has come out as an emerging tool to address the need of decision
makers and to cope with problems of uncertainties. A decision rule typically
contains procedures for combining criteria into a single composite index and a
statement of how alternatives are to be compared using this index. It is as
simple as threshold applied to a single criterion. It is structured in the
context of a specific objective. An objective is thus a perspective that serves
to guide the structuring of decision rules. To meet a specific objective, it is
frequently the case that several criteria will need to be integrated and
evaluated, called multi-criteria evaluations. Weighted linear combinations and
concordance-disconcordance analysis (Voogd, 1983 and Carver, 1991) are two most
common procedures in GIS based multi-criteria evaluations. In the former, each
factor is multiplied by a weight and then summed to arrive at a final
suitability index, while in the later, each pair of alternatives is analyzed for
the degree to which it outranks the other on the specified criteria. The former
is straight forward in a raster GIS, while the later is computationally
impractical when a large number of alternatives are present.
Information
vital to the process of decision support analysis, is rarely perfect in earth
sciences. This leads to uncertainties, which arises from the manner in which
criteria are combined and evaluated to reach a decision. When uncertainty is
present, the decision rule needs to incorporate modifications to the choice
function or heuristic to accommodate the propagation of uncertainty through the
rule and replace the hard decision procedures of certain data with soft-data of
uncertainty. Bayesian probability theory (Bonham-Carter et al., 1988; 1990;
1995), Dempster-Shafer theory (Cambell et al., 1982) and fuzzy set theory (Duda
et al., 1977) have been extensively in use in mineral targeting.
Theory of multi-criteria evaluation
Multi-criteria evaluation
is primarily concerned with how to combine the information from several criteria
to form a single index of evaluation. In case of Boolean criteria (constraints),
the solution usually lies in the union (logical OR) or intersection (logical
AND) of conditions. However, for continuous factors, a weighted linear
combination (Voogd, 1983) is a usual technique. As the criteria are measured at
different scales, they are standardised and transformed such that all factor
maps are positively correlated with suitability. Establishing factor weights is
the most complicated aspect, for which the most commonly used technique is the
pair-wise comparison matrix.
Evaluation of the relationship between
evidence (criteria) and belief is a forward chaining expert system. In this
system the propagation of favourability measure through the inference network
may include the Bayesian updating and fuzzy logic for computation of posterior
values of favourability given evidence(s). In the real world, the evidences and
hypotheses are uncertain. We cope with the problem by assigning probability
values to evidences (Duda et al., 1977). There is unidirectional propagation of
evidences through a hierachial network carries on towards an ultimate
hypotheses.
In a rule based inference system, the rules are usually of
the form:
If E1 and E2 and E3…………. and En, then H,
Where, Ei(i =
1,2……n) is the ith evidence and H is the associated hypotheses.
In a
full fledged inference net, many pieces of evidences are linked to a single
final hypotheses using the combination rules of conjunction, disjunction and
independence (Bayes).
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