Tropical cyclone forecaster integrated with case-based reasoning

James N.K. Liu; Chi Keung Simon Shiu; Jia You

doi:10.1007/978-0-387-85437-3_23

Tropical cyclone forecaster integrated with case-based reasoning

James N.K. Liu, Chi Keung Simon Shiu, Jia You

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

One of the major challenges for predicting tropical cyclone intensity is that we lack the understanding of coupling relationships of physical processes governing tropical cyclone intensification. This paper presents a Java-based case-based reasoning model to assist tropical cyclone forecasters to determine the intensity change of the tropical cyclone. Cases are constructed by using the data mining algorithms to uncover the hidden relationships between physical processes and tropical cyclone intensity. We specify the domain data, definitions of features from the data, tool for data exploration, and architecture of case-based reasoning model. Preliminary results are found to be useful to forecasters when faced with some unusual problem and under different weather situations.

Original language	English
Title of host publication	Proceedings of the European Computing Conference
Pages	235-241
Number of pages	7
Edition	VOL.2
DOIs	https://doi.org/10.1007/978-0-387-85437-3_23
Publication status	Published - 1 Dec 2009
Event	European Computing Conference - Athens, Greece Duration: 25 Sept 2007 → 27 Sept 2007

Publication series

Name	Lecture Notes in Electrical Engineering
Number	VOL.2
Volume	28 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	European Computing Conference
Country/Territory	Greece
City	Athens
Period	25/09/07 → 27/09/07

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

More information

10.1007/978-0-387-85437-3_23

Cite this

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title = "Tropical cyclone forecaster integrated with case-based reasoning",

abstract = "One of the major challenges for predicting tropical cyclone intensity is that we lack the understanding of coupling relationships of physical processes governing tropical cyclone intensification. This paper presents a Java-based case-based reasoning model to assist tropical cyclone forecasters to determine the intensity change of the tropical cyclone. Cases are constructed by using the data mining algorithms to uncover the hidden relationships between physical processes and tropical cyclone intensity. We specify the domain data, definitions of features from the data, tool for data exploration, and architecture of case-based reasoning model. Preliminary results are found to be useful to forecasters when faced with some unusual problem and under different weather situations.",

author = "Liu, {James N.K.} and Shiu, {Chi Keung Simon} and Jia You",

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Tropical cyclone forecaster integrated with case-based reasoning. / Liu, James N.K.; Shiu, Chi Keung Simon; You, Jia.
Proceedings of the European Computing Conference. VOL.2. ed. 2009. p. 235-241 (Lecture Notes in Electrical Engineering; Vol. 28 LNEE, No. VOL.2).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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Tropical cyclone forecaster integrated with case-based reasoning

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