Artificial Intelligence and Environmental Sustainability by Unknown

Author:Unknown
Language: eng
Format: epub
ISBN: 9789811914348
Publisher: Springer Nature Singapore

3 NFS for Real-Time Rainfall-Runoff Modelling

3.1 Fuzzy Inference System (FIS)

Zadeh [29] introduced the fuzzy logic based on the concept of fuzzy sets which are defined with no crisp boundary. This theory emulates the reasoning process in the human brain where approximate information and uncertainties are utilized to make decisions. Fuzzy logic works as a multi-valued logic (unlike Boolean logic that works with zero and one) where the values are defined by degrees of membership [30]. To implement fuzzy logic in fuzzy implication, the knowledge (information) is usually presented in the forms of fuzzy If-Then rules where fuzzy sets are used in the rules’ antecedents. The consequents of the rules, however, may or may not be defined by fuzzy sets.

Fuzzy inference system is an expert system that maps a given input to an output using fuzzy logic. A fuzzy inference system is composed of four major components (see Fig. 3): (i) a rule-base containing fuzzy if-then rules; (ii) an interface engine that performs inference operations on the rules; (iii) a fuzzification interface that transforms the crisp values into fuzzy values; and (iv) a defuzzification interface which transforms the fuzzy results into a crisp output [31]. FIS is classified into two main groups, namely linguistic and precise. In linguistic FIS, both antecedent and consequent of the fuzzy If-Then rules are defined by fuzzy sets. Perhaps the most well-known linguistic FIS is the Mamdani type [32]. On the other hand, in crisp FIS, the rules’ antecedents are defined by fuzzy sets, while the consequents are crisp values calculated by a specific mathematical function. The most commonly used precise FIS is Takagi-Sugeno FIS [33] where the rule consequent works by a weighted linear function of crisp input values.

Fig. 3The schematic architecture of a fuzzy inference system

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