SCHOOL OF COMPUTER SCIENCE AND SOFTWARE ENGINEERING
MONASH UNIVERSITY

TECHNICAL REPORT 2001/86



Seabreeze prediction using bayesian networks: a case study

R J Kennett, K B Korb and A E Nicholson

ABSTRACT

In this paper we examine the use of Bayesian networks (BNs) for improving weather prediction, applying them to the problem of predicting sea breezes.  We compare a pre-existing Bureau of Meteorology rule-based system with an elicited BN and others learned by two data mining programs, TETRAD II [?] and Causal MML [?].  These Bayesian nets are shown to significantly outperform the rule-based system in predictive accuracy.