Tropical Cyclone Forecaster Integrated with Case-Based Reasoning
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.
KeywordsTropical Cyclone Association Rule Tropical Cyclone Intensity Apriori Algorithm Tropical Cyclone Track
The authors would like to acknowledge the partial support of CRG grant G-U186 of The Hong Kong Polytechnic University.
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