Seabreeze prediction using Bayesian networks

Russell J Kennett, Kevin B Korb, Ann E Nicholson

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    25 Citations (Scopus)


    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 [Spirtes et al., 1993] and Causal MML [Wallace and Korb, 1999]. These Bayesian nets are shown to significantly outperform the rule-based system in predictive accuracy.
    Original languageEnglish
    Title of host publicationAdvances in Knowledge Discovery and Data Mining
    Subtitle of host publication5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16-18, 2001 Proceedings
    EditorsDavid Cheung, Graham J. Williams, Qing Li
    Place of PublicationBerlin Germany
    Number of pages6
    ISBN (Print)3540419101
    Publication statusPublished - 2001
    EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2001 - Hong Kong, China
    Duration: 16 Apr 200118 Apr 2001
    Conference number: 5th (Proceedings)

    Publication series

    NameLecture Notes in Artificial Intelligence
    ISSN (Print)0302-9743


    ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2001
    Abbreviated titlePAKDD 2001
    CityHong Kong
    Internet address

    Cite this