PNNL-13501 - Pacific Northwest National Laboratory

Study Control Number: PN99079/1407
Watershed Model and Land-Use Decision Support
Lance W. Vail, Mark S. Wigmosta, Duane Neitzel
Innovative approaches are required to transcend the structural differences in physical and biological models, if such
models are to be successfully linked to support natural resource management decision-making. Fuzzy computational
methods were shown to be a valuable tool in linking physical and biological models.
Project Description
The purpose of this project was to support a science-based
approach to natural resource management by assessing
the effectiveness of fuzzy computational methods in
linking advanced physical and biological models. We
developed several prototype fuzzy tools that were able to
incorporate “approximate reasoning” for representing
processes and for developing categorical conclusions
regarding the environment. We showed that fuzzy
methods provide both an intuitive and technically
defensible approach for linking physical and biological
models.
Introduction
This study represents the second year of a 3-year effort to
develop an integrated natural resource management
framework. A significant obstacle to successfully linking
physical and biological models was identified as the
fundamental structural differences between such models.
Physical models are more likely to involve a continuum
representation, whereas biological models are more likely
to rely on rules. Two significant issues, the pervasive
vagueness of rules and the multivaluedness associated
with temporal and spatial upscaling, suggested that fuzzy
methods might be useful in overcoming some of the
structural differences between physical and biological
models.
Fuzzy methods allow computing based on linguistic
variables (words) as opposed to numbers. Words are less
precise but closer to intuition and accommodate the
pervasive imprecision of biological process
understanding. Fuzzy computational methods exploit the
tolerance for imprecision, uncertainty, and partial truth to
achieve tractability, robustness, and efficient solutions.
Fuzzy methods complement but do not reduce the need
for probabilistic methods in either physical or biological
262 FY 2000 Laboratory Directed Research and Development Annual Report
process models. Whereas, randomness (probability)
describes the uncertainty of an event occurring, fuzziness
describes the degree to which an event occurs.
Approach
This project used a variety of fuzzy methods including:
fuzzy arithmetic, fuzzy logic, fuzzy clustering, and
adaptive neural fuzzy inference systems. Fuzzy
arithmetic was employed using standard programming
methods. The evaluations of fuzzy logic, fuzzy
clustering, and inference systems employed a commercial
software package. A series of rules and a database from
the Multispecies Framework Process was employed to
test the various fuzzy methods. These rules and data are
used to define aquatic habitat diversity in the Pacific
Northwest.
Results and Accomplishments
Fuzzy Arithmetic. To establish a clearer understanding of
basic fuzzy computational methods, we developed a fuzzy
rainfall runoff model. Fuzziness (expressed as a
membership function) was defined for saturated soil
hydraulic conductivity, effective soil porosity, soil depth,
and rainfall intensity. Fuzzy characteristics of habitat
capacity (river width and depth) were estimated using
standard fuzzy methods as a function of time. The fuzzy
results are reasonable based on comparison with other
(non-fuzzy) methods, but convey the uncertainty
associated with the fuzzy input parameters.
Fuzzy Logic. Fuzzy logic is an extension of multi-valued
logic. It is based around the concept of fuzzy sets. Fuzzy
sets relate to classes of objects with unsharp boundaries.
Membership in a fuzzy set is a matter of degree. Fuzzy
logic has been shown to be valuable for computing
process interactions and is currently employed in many
control applications (such as focusing camcorders,
automatic transmissions) and decision support systems