Tropical cyclone intensity forecasting model: balancing complexity and goodness of fit

Grace W Rumantir

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

    2 Citations (Scopus)


    Building forecasting models for tropical cyclone intensity is one of the most challenging area in tropical cyclone research. Most, if not all, of the existing models have been built using variants of Maximum Likelihood (ML) approach. The need to partition data into two sets for model development is seen to be one of the drawbacks of ML approach in the face of limited available data. This paper proposes a way to build forecasting model using a number of model selection criteria which take the penalized-likelihood approach, namely MML, MDL, CAICF, SRM. These criteria claim to have the mechanism to balance between model complexity and goodness of fit. The models selected are then compared with the benchmark models being used in operation.
    Original languageEnglish
    Title of host publicationPRICAI 2000 Topics in Artificial Intelligence
    Subtitle of host publication6th Pacific Rim International Conference on Artificial Intelligence Melbourne, Australia, August 28 - September 1,2000 Proceedings
    EditorsRiichiro Mizoguchi, John Slaney
    Place of PublicationBerlin Germany
    Number of pages11
    ISBN (Print)3540679251
    Publication statusPublished - 2000
    EventPacific Rim International Conference on Artificial Intelligence 2000 - Melbourne, Australia
    Duration: 28 Aug 20001 Sept 2000
    Conference number: 6th (Proceedings)

    Publication series

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


    ConferencePacific Rim International Conference on Artificial Intelligence 2000
    Abbreviated titlePRICAI 2000
    Internet address

    Cite this