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Current GIS applications are predominantly based on Boolean logic, a rigid two-valued mathematical system which does not take into account
From a human stand point, spatial relations between geographical entities are often expressed in an imprecise manner which can be interpreted only within a specific context. Inaccuracy and context dependency is an integral component of the human cognition and decision-making process.
Forecasts are generally beset with difficulties, since the effects of many factors and processes within ecosystems are insufficiently known. Similarly, uncertainty usually reigns concerning the scale of the relevant parameters or the development of factors of influence (e.g. the weather) over time.
We use expert knowledge in our method, and this knowledge may be uncertain and imprecise.
Situations where there is uncertainty in the determination of a criterion for the inclusion or exclusion of an element within a set, pose a problem of interpretation for the modeling of the landscape evolution. Uncertainties regarding the type and temporal course of factors of influence can be incorporated in models in the form of probabilities. In order to enhance the comprehension about the evolution of the environment, the method should manage the imprecision and the uncertainties inherent to the problem.
Fuzzy logic is an available method for approximating human reasoning and enhancing the level of intelligence in GIS. The fuzzy set theory suggests that the inclusion of an element depends on its degree of belonging. Thus, we can manage the inaccuracy and the uncertainty using the idea of fuzzy set theory. The results will be represented in a fuzzy image, which gives an idea about the evolution of the landscape.
