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According to literature, there are two aspects of a successful approach for seasonal forecasting of tropical cyclones, including factors relating to the formation and operation of the storms and regression methods. | Vietnam J Comput Sci 2016 3 81-91 DOI 10.1007 s4O595-016-0061-5 CrossMark REGULAR PAPER ENSO-based tropical cyclone forecasting using CF-ANFIS Trong Hai Duong 1 Phi Hung Do2 Sy Dzung Nguyen3 Minh Hien Hoang4 Received 21 December 2015 Accepted 20 February 2016 Published online 29 March 2016 The Author s 2016. This article is published with open access at Springerlink.com Abstract According to literature there are two aspects of a successful approach for seasonal forecasting of tropical cyclones including factors relating to the formation and operation of the storms and regression methods. Dealing with the factors El Nino-Southern Oscillation and other global factors such as Quasi-Biennial Oscillation Pacific Decanal Oscillation etc. and local factors such as sea surface temperature sea level pressures etc. were examined for tropical cyclone forecasting. For regression the most previous works used the linear regression-based model for seasonal tropical forecasting. However the seasonal tropical forecasting requires high-dimensional data so the forecasting ability using linear regression will have drawback. In this work we analyse literatures of forecasting factors and regression methods for tropical cyclone forecasting. A CF-ANFIS algorithm integrating a conjunct space cluster and Cascadeforward neural network are proposed to forecast the number of tropical cyclone making landfall. This algorithm resolves the drawback by considering all forecast factors with high B Trong Hai Duong haiduongtrong@gmail.com Phi Hung Do dophihung@gmail.com Sy Dzung Nguyen nsidung@yahoo.com Minh Hien Hoang hnm@netnam.vn 1 International University VNU-HCMC Ho Chi Minh City Vietnam 2 Nguyen Tat Thanh University Ho Chi Minh City Vietnam 3 Department of Mechanical Engineering Ho Chi Minh University of Industry HUI Ho Chi Minh City Vietnam 4 Disaster Management Centre Ministry of Agriculture and Rural Development Ho Chi Minh City Vietnam dimensional data. The experimental results indicated .
