An intelligent CBR model for predicting changes in tropical cyclones intensities

The intensity of a tropical cyclone is an important factor in determining its direction ofmovement. This chapter presents an intelligent case-based reasoning system to help tropical cycloneforecasters predict changes in tropical cyclone intensities. Using best track data from the Joint Typhoon Warning Centre, we extract data for the coordinates, speed, direction, season and atmospheric pressure of tropical cyclones, treating each sixhour observation as a case and classifying them in three groups, “South China Sea”, “Philippines and Taiwan” and “Japan”. Similar cases are constructed by using Apriori association rule (AR) mining model, which is built-in in data mining software—SPSS Clementine 10.1, to reveal the hidden relationships between the meteorological environment and TC intensity change. Rules generated from the model are used to predict changes of intensities of TCs. Experimental results indicate that the proposed intelligent model is able to provide reasonable forecast in severe weather. In particularly, it is found that the accuracy of forecast is strongly related to the track pattern and the number of cases that are not located in the three location groups.