A fuzzy logic based methodology for knowledge acquisition is used to build a
basic case-based reasoning system: a fuzzy k-nearest neighbor based
prediction system. The methodology is used to acquire knowledge about
what salient features of continuous-vector, temporal cases indicate significant
similarity between cases. Such knowledge is encoded in a similarity-
measuring function and thereby used to retrieve k nearest neighbors (k-nn)
from a large database. Predictions for the present case are made from a
weighted median of the outcomes of analogous past cases, the k-nn. Past
cases are weighted according to their degree of similarity to the present case.
Fuzzy logic imparts to case-based reasoning the case-discriminating ability of
a domain expert. Fuzzy methods represent cases with any combination of
words and numbers. The fuzzy k-nn technique retrieves similar cases by
emulating a domain expert who understands and interprets similar cases.
The major contribution of fuzzy logic to case-based reasoning (CBR) is that it
enables us to directly acquire domain knowledge about feature salience. This
enables us to retrieve a few most similar cases from a large database. This
in turn helps us to avoid adaptation problems.
Such a fuzzy k-nn system enables a new form of "persistence climatology"
(PC), which is widely recognized as formidable benchmark for very-short-
range weather prediction. PC has until now had built-in limitations because it
uses an eager learning approach, whereas the fuzzy k-nn system has no
such built-in limitations because it uses a lazy learning approach.
Meteorologists regard such a fuzzy k-nn forecasting system as "custom
climatology on-the-fly." Such a system for making airport weather predictions
will let us tap many, large, unused archives of airport weather observations,
ready repositories of temporal cases. This will help to make airport weather
predictions more accurate, which will make air travel safer and make airlines
more profitable.
Accordingly, a fuzzy k-nn based prediction system, called WIND-1, is
proposed, implemented, and tested. Its unique component is an expertly-
tuned fuzzy k-nn algorithm with a temporal dimension. It is tested with the
problem of producing 6-hour predictions of cloud ceiling and visibility at an
airport, given a database of 300,000 consecutive hourly airport weather
observations (34 years of record). Its prediction accuracy is measured with
standard meteorological statistics and compared to a benchmark prediction
technique, persistence. In realistic simulations, WIND-1 is significantly more
accurate.
