Abstract
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.
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Rumantir, G.W. (2000). Tropical Cyclone Intensity Forecasting Model: Balancing Complexity and Goodness of Fit. In: Mizoguchi, R., Slaney, J. (eds) PRICAI 2000 Topics in Artificial Intelligence. PRICAI 2000. Lecture Notes in Computer Science(), vol 1886. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44533-1_26
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DOI: https://doi.org/10.1007/3-540-44533-1_26
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