Subject: Weather prediction using FST
Date: Tue Aug 22 2000 - 18:57:22 MET DST
Thesis: Weather Prediction Using Case-Based Reasoning and Fuzzy Set
A fuzzy logic based methodology for knowledge acquisition is developed
and used for retrieval of temporal cases in a case-based reasoning
system. The methodology is used to acquire knowledge about what salient
features of continuous-vector, unique 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
(i.e., the k-nn, or the analog ensemble). Past cases are weighted
according to their degree of similarity to the present case.
Fuzzy logic imparts to case-based reasoning the perceptiveness and
case-discriminating ability of a domain expert. The fuzzy k-nn technique
retrieves similar cases by emulating a domain expert who understands and
interprets similar cases. The main contribution of fuzzy logic to
case-based reasoning (CBR) is that it enables us to use common words to
directly acquire domain knowledge about feature salience. This knowledge
enables us to retrieve a few most similar cases from a large temporal
database, which in turn helps us to avoid the problems of case
adaptation and case authoring.
Such a fuzzy k-nn weather prediction system can improve the technique of
persistence climatology (PC) by achieving direct, efficient, expert-like
comparison of past and present weather cases. PC is an analog
forecasting technique that is widely recognized as a formidable
benchmark for short-range weather prediction. Previous PC systems have
had two built-in constraints: they represented cases in terms of the
memberships of their attributes in predefined categories and they
referred to a preselected combination of attributes (i.e., cases defined
and selected before receiving the precise and numerous details of
present cases). The proposed fuzzy k-nn system compares past and present
cases directly and precisely in terms of their numerous salient
attributes. The fuzzy k-nn method is not tied to specific categories,
nor is it constrained to using only a specific limited set of
predictors. 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 over 300,000
consecutive hourly airport weather observations (36 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. WIND-1 produces forecasts at the rate of about one per minute.
The thesis is online at http://www.cs.dal.ca/~bjarne/thesis
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