PNNL-13501 - Pacific Northwest National Laboratory

(portfolio selection). We developed a tool called Fuzzy
Hab. Fuzzy Hab is used to estimate habitat diversity from
a set of categorical statements about the environment
(embeddedness, icing, pool spacing, riparian function, and
woody debris). Each of these categorical statements is
vaguely defined. Estimates for each categorical statement
are derived from physical process models. Fuzzy Hab
supports a variety of canonical forms of membership
functions and generates source code in C that can be
transferred to any platform for integration into a natural
resource modeling framework.
Fuzzy Clustering. The purpose of clustering is to identify
groupings from a large data set to produce a concise
representation of a system’s behavior. Allowing
membership in a group to be fuzzy (i.e., a matter of
degree) results in a considerably more flexible and robust
representation of the system. We reviewed recent
literature where fuzzy clustering had been applied in
natural resource management. We determined that fuzzy
clustering characterizes data richness and complexity,
easily adapts to new data, and is not particularly sensitive
to type of membership function chosen.
Adaptive Neural Fuzzy Inference Systems. Adaptive
inference systems provide a method to develop fuzzy
rules from large but sparse environmental datasets. Data
goes in and rules come out. In this project, fuzzy rules for
making categorical conclusions of aquatic habitat
diversity were derived from partially complete tables
developed for the Pacific Northwest Multispecies
Framework Process. Inference systems are able to
generalize large datasets while developing rules that are
less prone to reflect arbitrary and sometimes conflicting
threshold effects.
Summary and Conclusions
Fuzzy computational methods are useful in overcoming
the structural differences between physical and biological
models. While initially fuzzy methods were expected to
benefit mostly biological models through improved
categorical representation of the physical environment,
they are now expected to have significant benefit to
physical process representation such as for
geomorphological processes critical to habitat.
We conclude that fuzzy methods
• provide a compact and flexible process representation
• accommodates the pervasive imprecision of process
understanding
• exploit the tolerance for imprecision, uncertainty, and
partial truth to achieve tractability, robustness, and
low-cost solutions
• exploit neural networks’ ability to use large, sparse
datasets in developing rules.
As this project continues during the next fiscal year, fuzzy
methods will be incorporated into a web-based integrated
natural resource management framework.
Earth System Science 263