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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1886 LNAI, Issue , 2000, Pages 230-240

  Rumantir, Grace W ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

Industrial applications of artificial intelligence; Knowledge discovery and data mining; Tropical cyclone intensity forecasting

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; FORECASTING; MAXIMUM LIKELIHOOD; STORMS; TROPICS;

FORECASTING MODELING; INDUSTRIAL APPLICATIONS OF ARTIFICIAL INTELLIGENCES; KNOWLEDGE DISCOVERY AND DATA MININGS; MAXIMUM LIKELIHOOD APPROACHES; MODEL SELECTION CRITERIA; PENALIZED LIKELIHOOD; TROPICAL CYCLONE INTENSITY; TROPICAL CYCLONE INTENSITY FORECASTING;

HURRICANES;

EID: 35248870207     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44533-1_26     Document Type: Conference Paper

References (14)

1. H. Bozdogan. Model selection and akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika, 52(3):345-370, 1987.
2. C.W. Landsea, W.M. Gray, P.W. Mielke Jr. and K.J. Berry. Seasonal forecasting of atlantic hurricane activity. Weather, 49(8):273-284, August 1994.
3. M. DeMaria and J. Kaplan. A statistical hurricane intensity prediction scheme (SHIPS) for the Atlantic basin. Weather and Forecasting, 9(2):209-220, June 1994.
4. P.J. Fitzpatrick. Forecasting cyclone intensity change in the West Pacific. In Proceedings of the 21st Conference on Hurricanes and Tropical Meteorology, pages 94-96, Miami, Florida, 1995. American Meteorological Society.
5. W.M. Gray. Seasonal forecasing. In G.J. Holland, editor, Global Guide to Tropical Cyclone Forecasting, chapter 5. World Meteorological Organization (in press), Geneva - Switzerland, 1999. available: http://www.bom.gov.au/bmrc/meso/ New/wmocas.pubs/global.guide/globa-guide-intro.htm.
6. W.M. Gray and C.W. Landsea. African rainfall as a precursor of hurricane-related destruction on the us east coast. Bulletin of American Meteorological Society, 73(9): 1352-1364, September 1992.
7. B.R. Jarvinen and C.J. Neumann. Statistical forecasts of tropical cyclone intensity. Technical Report Tech. Memo. NWS NHC-10, National Oceanic and Atmospheric Administration (NOAA), Miami, Florida, 1979.
8. C.W. Landsea. SHIFOR94 - atlantic tropical cyclone intensity forecasting. In Proceedings of the 21st Conference on Hurricanes and Tropical Meteorology, pages 365-367, Miami, Florida, 1995. American Meteorological Society.
9. C.J. Neumann. An alternate to the HURRAN tropical cyclone forecasting system. Technical Memo NWS-62, NOAA, 1972.
10. J. Rissanen. Modeling by shortest data description. Automatica, 14:465-471, 1978.
11. J. Rissanen. Stochastic complexity. Journal of the Royal Statistical Society B, 49(1):223-239, 1987.
12. G.W. Rumantir. Minimum Message Length criterion for second-order polynomial model discovery. In T. Terano, H. Liu, A.L.P. Chen, editor, Knowledge Discovery and Data Mining: Current Issues and New Applications, PAKDD 2000, LNAI 1805, pages 40-48. Springer-Verlag, Berlin Heidelberg, 2000.
13. V. Vapnik. The Nature of Statistical Learning Theory. Springer, New York, 1995.
14. C.S. Wallace and P.R. Freeman. Estimation and inference by compact coding. Journal of the Royal Statistical Society B, 49(1):240-252, 1987.