Two decision analysis approaches: Choosing a chemical analysis method

This article illustrates how two different decision analysis (DA) methods can be used to assist in making environmental remediation decisions. The two DA methods are Multiattribute Utility Theory (MAUT) and the Analytic Hierarchy Process (AHP). MAUT and AHP differ greatly in their underlying philosophies. MAUT employs an interval scale and constructs utility functions that quantify the total utility to the decision maker of each technology alternative; AHP employs a ratio scale and uses pairwise comparisons to produce a final ranking of the alternatives that reflects the decision maker's comparative preferences. In this study, the same technology was ranked first by both methods; however, the remaining rankings did not agree. Also, statistical analysis indicated that some score differences may be statistically insignificant. These results are presented and discussed along with a comparison of the features, advantages, and disadvantages of these two DA approaches.     

Using fuzzy set theory to address the uncertainty of susceptibility to drought

This paper presents the technical aspects of a new methodology for assessing the susceptibility of society to drought. The methodology consists of a combination of inference modelling and fuzzy logic applications. Four steps are followed: (1) model input variables are selected—these variables reflect the main factors influencing susceptibility in a social group, population or region, (2) fuzzification—the uncertainties of the input variables are made explicit by representing them as ‘fuzzy membership functions’, (3) inference modelling—the input variables are used to construct a model made up of linguistic rules, and (4) defuzzification—results from the model in linguistic form are translated into numerical form, also through the use of fuzzy membership functions. The disadvantages and advantages of this methodology became apparent when it was applied to the assessment of susceptibility from three disciplinary perspectives: Disadvantages include the difficulty in validating results and the subjectivity involved with specifying fuzzy membership functions and the rules of the inference model. Advantages of the methodology are its transparency, because all model assumptions have to be made explicit in the form of inference rules; its flexibility, in that informal and expert knowledge can be incorporated through ‘fuzzy membership functions’ and through the rules in the inference model; and its versatility, since numerical data can be converted to linguistic statements and vice versa through the procedures of ‘fuzzification’ and ‘defuzzification’.