FUZZY KNOWLEDGE REPRESENTATION AND ENVIRONMENTAL RISK ASSESSMENT

FUZZY KNOWLEDGE REPRESENTATION AND ENVIRONMENTAL RISK ASSESSMENT

Abstract

Knowledge Representation (KR) is the key aspect towards the development of Intelligent Systems. Knowledge can be represented by employing Probabilistic methods, Bayesian and Subjective Bayesian models and also Fuzzy Algebra functions and Relations.
When Zadeh introduced the first Fuzzy Logic concepts in 1965, he stated that in the near future we will be calculating with words. This has been successfully achieved today and by using Fuzzy Algebra tools we are able to give a mathematical meaning to real world concepts like "hot room", "tall man" or "young man".

Fuzzy Algebra is not only used to model uncertainty or vague concepts, but it enables us to calculate with proper words called "Linguistics", especially in modern control systems. By using Fuzzy Sets and their corresponding Membership Functions we can build Rule Based Intelligent Systems that use Fuzzy "IF" THEN" rules. Also Fuzzy Databases can be developed to perform SQL queries in a more intelligent manner.

T-Norms and S-Norms operators can be applied towards performing conjunction or disjunction operations between Fuzzy Sets and offer us various modeling perspectives of a specific problem.
Finally Fuzzy c-means and Fuzzy Adaptive clustering, offer flexible clustering operations and they have advantages over traditional statistical methods.

All of these Fuzzy principles have been applied towards the development of Natural disasters (floods and forest fires) Risk estimation Systems. These systems have proven their validity and their potential use in a wider scale in the future.