Designing Interpretations
Designing and Developing InterpretationsSeptember 16-18,
2014NSSC, Lincoln, NE
Part IIIWelcome to D&D.1Review of YesterdayWednesdayReview
property scriptsDescription of EvaluationsEvaluation styles and
when to use themContinuous variableText
variableInvertedLimitationSuitabilityDeveloping Evaluations (Fuzzy
Sets)Boundaries, SplinesWork on project evaluations2Preview of
TodayThursdayReview Properties and EvaluationsBase
RulesSubrulesParent RulesOperatorsHedges WeightingLocal Extreme
Conditions (Al sat, LEP, gyp)Model TechniquesProductivity Index
discussionTesting and Validation
3Charles KelloggReliable interpretations can result only from a
synthesis of basic data about the soils themselves, obtained from
field and laboratory research, data from field experiments, and the
experience of users of soils, especially farmers, ranchers,
foresters, and engineers.
This is Jackson Pollock in soil. Note the unfortunate join
conditions.4The Overarching Truth in NASISThere are as many ways to
skin cats as there are cats.In other words, there is more than one
way to do most things.I stick with what I can understand, what
works, and as much as possible is economic of code
I do not advocate literally skinning cats. What works is derived
by experimentation and perseverance.5Rule : DefinitionThere are
several terms you will hear dealing with rules:Base Rule The lowest
level of the rule structure, has one or more evaluations attached
to it, provides rating information on a soil attribute.Sub Rule An
intermediate rule level, may have evaluations attached to it, but
may be made up of base rules, but is not a stand alone rule,
provides information on soil attributes or related dataPrimary Rule
The highest level of rule, provides the overall rating for the land
useChild Rule A rule that is attached to a parentParent Rule A rule
that has children attachedMain Rule About the same as Primary Rule,
the highest level rule in a context
Think of rule level like a soil. The depth of 0 is at the top
and down from there, the depth increases as the level goes down.
Rule level 0 is the main rule in a context.6Base Rule :
FunctionAccepts the fuzzy number from the evaluation that resulted
from rating of the data extracted by the linked property scriptIn
the simplest guise, is linked to one evaluationReturns a fuzzy
number and a rating class nameMight also think of this as a rating
reason
We will talk about hedges later.7Sub Rule : FunctionA Sub Rule
can be used to group the outputs of lower level rulesUseful to
group rules to see what is causing a rating to be unexpectedUsually
see these constructs in more complex interpretations
8Primary Rule : FunctionThe Primary Rule returns the overall
fuzzy number and the defuzzified rating class name for the
component
You could produce an interpretation composed entirely of
evaluations. But, there would be no reasons.9OperatorsThe operator
to use in a rule depends on what is being modeled and howThe
operator selected will fit the logic of what you are
doingExperimentation is also used in some instances to see what
makes the best resultOperatorsSelects the maximum fuzzy number
returned from a set of rules; thus, often used in limitation style
interpretations
Selects the minimum fuzzy number from a set of rules; thus,
often used in suitability style interpretations
Finds the product of the fuzzy numbers from the attached rules,
a way of modeling interaction of variables
11OperatorsFinds the sum of the fuzzy number returned from any
number of rules; thus, often used where effects have been weighted
(but need to remember saturation)
Calculates the arithmetic mean of the fuzzy numbers from the set
of attached rules
Plus sign - Finds the sum of two and only two fuzzy numbers from
two attached rules (remember fuzzy numbers cannot exceed 1)
12OperatorsMinus sign - Finds the difference between the fuzzy
number returned from two and only two rules (but need to remember
fuzzy numbers cannot be less than zero)
Asterisk - Finds the product of two and only two fuzzy numbers
from two attached rules
13HedgesHedges allow you to change fuzzy numbers based on a more
or less linguistic basisPuts numbers to language in a consistent
mannerWe will examine some of the ones that are used or look
interesting
HedgesThe ADD hedge allows you to bump a fuzzy number up by a
set amount, affecting all fuzzy numbers the same way
The MULTIPLY hedge allows you to increase or decrease alpha by
multiplication, I often add this hedge before all the sub rules in
a rule and set the parameter to 1
The POWER hedge can be used to increase (less than 1) or
decrease (greater than 1) the fuzzy numbers, except for 1, the
value of the parameter is typically found iteratively
Hedges allow you to change a fuzzy number based on some
conventional wisdom. These are the ones I have used.15HedgesThe
NULL NOT RATED hedge allows you send an unambiguous decision to the
rule, which propagates up to the highest level, when critical data
is missing (null), the indeterminant null
The NOT NULL AND hedge sends a zero fuzzy number to the rule if
data is null, this is a determinant null, often used with
limitation style interpretations, can be used to check for a
condition
The NULL OR hedge sends a 1 to the rule if data is null, this is
a determinant null, often used with suitability style
interpretations, can be used to check for a condition
Determinant null means the data is null by convention and you
can provide the right answer by convention.16Using NOT NULL AND
There is a family of evaluations depicting the effect of wetness
on corn yield with varying growing season length. The component
will fall into one frost-free-day class. A fuzzy number is returned
when this happens. All the other conditions are null but set to 0
by the hedge. The OR operator then takes the highest fuzzy number,
which should be the only one that is not 0.17HedgesThe NOT hedge
takes 1 minus the fuzzy number (called A or Alpha) which basically
inverts the result
The ALPHA hedge allows you to set a fuzzy number to zero if it
goes below the specified value IF A < 0.5 THEN 0 ELSE A
The LIMIT hedge allows you to set an upper limit on A IF A
>0.5 THEN 0.5 ELSE A
18Weighting VariablesAdjust the impact of a single attribute
Balance the relative impact of variables (which are most
influential or difficult to overcome?)Hedge (multiply, add,
subtract, divide, power)Adjust evaluation
Hedges (multiply, add, power, etc)Weighting using an
evaluationFull effect
Reduced effect
Works on the domain of the data. Since clay activity cannot go
below 0, the highest alpha this evaluation can reasonable return is
about 0.75 and clay activity cannot cause a component to be rated
severely limited.20Weighting using the evaluationFull effect
Reduced effect
Works on the domain of the data. Since clay activity cannot go
below 0, the highest alpha this evaluation can reasonably return is
0.8 and clay activity cannot cause a component to be rated severely
limited.21Weighting in the RuleIn this case, the sub rules are
weighted about equally using MULTIPLY hedges and a SUM operator
Weights in the rule affect the output of the entire rule
The value of the MULTIPLY parameter is sometimes established
iteratively
Productivity Indices Discussion:Huddleston PaperTNCCPIStorie
IndexCPI seriesNCCPIMissouri effortsIndiana Corn
Interesting gadgets?Data concerns?Improvements? I apologize for
the productivity index bend, but that is what I have been up to
lately23Testing and Validation: IN Corn ExampleCEC shows a
peculiarity, could be a data extraction problem
Soil moisture data shows a possible lack of DRAINED local
phase
This exercise could go on for a long time. Bottom line is that
interpretations are data driven and sometimes our preconceived
notion of a response may be at odds with the data.24Continue
In-class Projects
