September 16-18, 2014 NSSC, Lincoln, NE Part III

 Wednesday Review property scripts Description of Evaluations Evaluation styles and when to use them  Continuous variable  Text variable  Inverted  Limitation  Suitability  Developing Evaluations (Fuzzy Sets)  Boundaries, Splines Work on project evaluations

 Thursday Review Properties and Evaluations Base Rules Subrules Parent Rules Operators Hedges Weighting Local Extreme Conditions (Al sat, LEP, gyp…) Model Techniques Productivity Index discussion Testing and Validation

 “Reliable 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.”

 There 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

 There 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 data Primary Rule – The highest level of rule, provides the overall rating for the land use Child Rule – A rule that is attached to a parent Parent Rule – A rule that has children attached Main Rule – About the same as Primary Rule, the highest level rule in a context

 Accepts the fuzzy number from the evaluation that resulted from rating of the data extracted by the linked property script  In the simplest guise, is linked to one evaluation  Returns a fuzzy number and a rating class name  Might also think of this as a “rating reason”

 A Sub Rule can be used to group the outputs of lower level rules Useful to group rules to see what is causing a rating to be unexpected Usually see these constructs in more complex interpretations

 The Primary Rule returns the overall fuzzy number and the defuzzified rating class name for the component

 The operator to use in a rule depends on what is being modeled and how  The operator selected will fit the logic of what you are doing  Experimentation is also used in some instances to see what makes the best result

 Selects 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

 Finds 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)

 Minus 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

 Hedges allow you to change fuzzy numbers based on a more or less linguistic basis  Puts numbers to language in a consistent manner  We will examine some of the ones that are used or look interesting

 The 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

 The 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

 The 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”

 Adjust 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)

 Full effect  Reduced effect

 Full effect  Reduced effect

 In 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

 TNCCPI  Storie Index  CPI series  NCCPI  Missouri efforts  Indiana Corn …  Interesting gadgets?  Data concerns?  Improvements? …

 CEC shows a peculiarity, could be a data extraction problem  Soil moisture data shows a possible lack of “DRAINED” local phase