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).

see on
http://www.gisdevelopment.net/application/geology/mineral/geom0002a.htm