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\author{\noun{Richard B. Shepard}}


\title{Quantifying Environmental Impact Assessments Using Fuzzy Logic}

\maketitle
\thispagestyle{empty}

Richard B. Shepard, Ph.D.

Applied Ecosystem Services, Inc.

2404 SW 22 Street

Troutdale, OR 97060-1247

U.S.A.

rshepard@appl-ecosys.com

\null\vfill

Copyright \copyright 2004 Springer-Verlag New York, Inc.

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\chapter*{Preface}

\addcontentsline{toc}{chapter}{Preface}





Formal requirements for the assessment of environmental impacts of
development activities may have begun in the United States with the
passage of the National Environmental Policy Act (NEPA) in 1969, but
it now is found in more than 200 countries world-wide. The details
vary, but the underlying goals of minimizing environmental degradation
and improving environmental conditions are the same. In many countries,
these national requirements are supplemented by additional requirements
by states, provinces, counties, cities and other political divisions
that are collectively called sub-national statutes and regulations.

I am most familiar with the requirements at the national level in
the United States as well as at the state and county levels in the
western half of the country. However, books, other published reports
and communications with peers amply document that problems caused
by the subjective nature of environmental impact assessments are international
in scope. This subjectivity can be quantified and treated with mathematical
rigor by the application of advanced computational intelligence techniques.
This approach will work equally well regardless of geographic location
or political jurisdiction because it is responsive to variations in
societal values, legal frameworks and regulatory agency practices.
Trans-national organizations such as the European Union, World Bank
and United Nations Environmental Program also set project environmental
standards that must be met in addition to the standards set by local
governments.

It is important to emphatically make this disclaimer: in no way is
this book to be taken as criticism of environmental impact assessment
(EIA) laws, regulations, practitioners or theorists. Such criticism
would be neither warranted nor justified. Identifying subjective aspects
is not criticism. Such identification forms the basis for understanding
this book and benefiting from its content.

This book has three objectives:

\begin{enumerate}
\item To document how environmental impact assessments have been conducted
and to explain when and why contention develops. The book stresses
that environmental assessments (whether of impacts or of existing
conditions) are subjective expressions of societal, group and individual
values and opinions. As such, they are not objective or measurable.
Science, particularly ecology and environmental science, has difficulties
dealing with such {}``non-scientific'' concepts as feelings, beliefs
and values.\\
\\
The specifics of the EIA process vary with the controlling jurisdiction;
there is no attempt to describe all the variations and subtle differences
because this is not a book to teach the theory and detailed practice
of environmental impact assessments as implemented world-wide. However,
specific points will be based on my experiences as well as what others
have experienced and described in the published literature.\\

\item The first objective established two important points: EIAs are subjective
and the methods created over the years do not effectively handle the
subjectivity. We speak and write using terms that cannot be measured.
Concepts such as \emph{Significan}t, \emph{Distant}, \emph{Acceptable}
and others are understood by everyone -- but we each may have a different
definition of these terms. Almost all environmental regulatory processes
depend on such imprecise, vague, inherently uncertain terms. Commercial
development may be prohibited on steep slopes, but how is steep defined?
It is almost always an arbitrary, crisp value; for example, 20 percent.
This does not mean that a slope of 19.5 percent is not steep, but
it means that there is no sharp threshold that separates steep from
not steep. Fortunately, fuzzy logic and approximate reasoning (among
other computational intelligence methods) handle the subjectivity
effectively and with mathematical rigor. Therefore, the second objective
is to justify the use of fuzzy logic and approximate reasoning to
provide decision makers with the ability to make informed decisions;
ones that are technically sound and legally defensible. I do this
by describing core issues of an environmental impact assessment in
terms of fuzzy modeling and other computational intelligence techniques.\\
\\
The concept of fuzzy sets was developed explicitly to address the
inherent imprecision of everyday language which we all use to express
ideas that cannot be measured. Fuzzy logic is the mathematics that
permit rigorous operations on fuzzy sets to arrive at a outcome that
is meaningful and can be explained. Approximate reasoning is the computer
modeling of how humans make decisions (IF this THEN do that) when
all the input data are subjective and not directly measurable. Other
advanced techniques of artificial intelligence (AI) (including expert
systems, decision support systems and data mining using neural networks
and evolutionary algorithms) also can be effectively and productively
applied to addressing the underlying purposes of environmental impact
assessments.\\

\item The third objective is to illustrate the use of computational intelligence
techniques presented in objective 2 for environmental impact assessment.
This example creates an approximate reasoning model applied to a project
completed the traditional way under Washington state laws and regulations.
While the example is based on a real industrial development proposal,
the original environmental impact assessment was not developed with
computational techniques. Therefore, the example has been adapted
to demonstrate the application of these tools by adding missing information
and deleting some components to make the example a reasonable size.
\end{enumerate}

\section*{Acknowledgments}

Among all the people whose efforts have brought me to the level of
understanding and experience that allows me to write knowledgeably
about environmental impact assessments and fuzzy system models a handful
stand out of the crowd. Earl Cox introduced me to fuzzy sets and fuzzy
logic with the first edition of his {}``The Fuzzy System Handbook''
a decade ago. Since then his friendship and insight have been critical
to my education in this field and the subtleties of writing software
that function to compute fuzzy system models with parallel rule firing
and the ability to solve problems otherwise intractable. Three other
friends and professional colleagues deserve public thanks for the
highly productive conversations we have had over the years on environmental
issues and running natural resource industries. These friends, Johathon
Brown, Paul Scheidig and Ivan Urnovitz -- as well as Earl Cox -- manfully
read through drafts of the book and let me know at what sections their
eyes started to glaze over. Their suggestions for elucidation and
clarity make this book a much better work. My editor at Springer-Verlag,
Janet Slobodian, has been a great guide into the world of book publishing;
a world much different from that of peer-reviewed scientific or trade
journal publishing. Despite the best efforts of all these outstanding
people, any errors or mistakes that remain are mine alone.

August 2004\\
Troutdale, Oregon

%
\begin{table}
\begin{center}Mathematical symbols used in fuzzy logic (from \citep{Cox99})\end{center}

\begin{center}\begin{tabular}{cl}
\hline 
Symbol&
\multicolumn{1}{c}{Meaning}\tabularnewline
\hline
\hline 
$\neg$&
set NOT (also complement or inversion)\tabularnewline
\hline 
$\cap$&
set AND (also intersection operator)\tabularnewline
\hline 
$\cup$&
set OR (also union operator)\tabularnewline
\hline 
$\aleph$&
higher dimensional fuzzy space\tabularnewline
\hline 
{[}x,x,x{]}&
fuzzy membership value\tabularnewline
\hline 
$\in$&
member of a set; within\tabularnewline
\hline 
poss(x)&
the possibility of event x\tabularnewline
\hline 
prob(x)&
the probability of event x\tabularnewline
\hline 
\{x\}&
crisp, or Boolean, membership function\tabularnewline
\hline 
$\bullet$&
dyadic operator\tabularnewline
\hline 
$\xi(x)$&
expected value of a fuzzy region\tabularnewline
\hline 
$\mu$&
fuzzy membership function\tabularnewline
\hline 
$\propto$&
proportionality\tabularnewline
\hline 
$\mu(x)$&
membership, or truth, function in fuzzy set\tabularnewline
\hline 
$\Re$&
element from domain of fuzzy set\tabularnewline
\hline 
$\otimes$&
Cartesian product or space\tabularnewline
\hline 
$\oslash$&
empty, or null, set\tabularnewline
\hline 
$\supset$&
implication\tabularnewline
\hline 
$\wedge$&
logical AND\tabularnewline
\hline 
$\vee$&
logical OR\tabularnewline
\hline 
$\Sigma$&
summation\tabularnewline
\hline
\end{tabular}\end{center}
\end{table}


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