Environmental Engineering Reference
In-Depth Information
Chapter 10
Modelling Ecological Processes with Fuzzy
Logic Approaches
Agnese Marchini
Abstract The development of an ecological model may involve problems of uncer-
tainty. Ecologists have to deal with imprecise data, ecosystem variability, complex
interactions, qualitative aspects, and expert knowledge expressed in linguistic terms.
In all these cases, fuzzy logic could provide a suitable solution. Fuzzy logic allows to:
use uncertain information such as individual knowledge and experience; to combine
quantitative and qualitative data; to avoid artificial precision and to produce results
that are found more often in the real world. Developed in the late sixties as a method
to create control systems when using imprecise data, fuzzy logic has been used for a
very large number of engineering applications, and more recently to develop models
of air, water and soil ecosystems.The following sections of this chapter introduce the
basic structure of a fuzzy model, describing the variety of options that exist at each
stage. An example of fuzzy model is also outlined: the knowledge-driven develop-
ment of an index of water quality having five qualitative output classes. Finally,
possible future developments of fuzzy modelling in ecology are suggested.
10.1
Introduction
10.1.1 Fuzzy Logic: A Mathematical Theory for Uncertainty
Ecologists very frequently cope with imprecise and vague data. Formalizing such
fragmentary information with traditional “hard computing” approaches can be
difficult. Important alternative tools are provided by the so-called “soft computing”
techniques. They fill a methodological gap, dealing with imprecision, uncertainty,
partial truth, and approximation. Fuzzy logic, a theory developed in the mid-1960s
