Seabreeze prediction using Bayesian networks

Russell J Kennett, Kevin B Korb, Ann E Nicholson

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

    19 Citations (Scopus)

    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 [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
    PublisherSpringer
    Pages148-153
    Number of pages6
    ISBN (Print)3540419101
    DOIs
    Publication statusPublished - 2001
    EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2001 - Hong Kong, Hong Kong
    Duration: 16 Apr 200118 Apr 2001
    Conference number: 5th
    https://link.springer.com/book/10.1007/3-540-45357-1 (Proceedings)

    Publication series

    NameLecture Notes in Artificial Intelligence
    PublisherSpringer
    Volume2035
    ISSN (Print)0302-9743

    Conference

    ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2001
    Abbreviated titlePAKDD 2001
    CountryHong Kong
    CityHong Kong
    Period16/04/0118/04/01
    Internet address

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