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General Information
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2017 Vol.7(6): 352-356 ISSN: 2010-3697
DOI: 10.7763/IJMO.2017.V7.611

Predicting the Typhoons in the Philippines Using Radial Basis Function Neural Network

Jane Colleen T. Ventura, R. B. Angelou F. Cobosa, Angie M. Ceniza, and Kris A. Capao
Abstract—Typhoons, also called hurricanes in the Atlantic or North Pacific, are one of the natural disasters that have caused a great amount of damage in the Philippines. A few typhoons have caused the loss of lives and the loss of resources. Over the years, several typhoon prediction models have been created through different ways such as through regression and data mining techniques. This research built a Radial Basis Function Neural Network that would predict typhoons in the Philippines. Historical typhoon data was taken from Philippines Atmospheric Geophysical and Astronomical Services Administration (PAGASA) and the historical weather data of an area in the Northwestern Pacific Basin, specifically Guam, was taken from a source online. Weather and typhoon data for the first 13 years served as training set; the remaining two years served as testing set. The accuracy of the Radial Basis Function Network was tested using cross entropy error and root mean square error. The cross entropy error is 120.941, and the root mean square error is 9.570.

Index Terms—Typhoons, radial basis function neural network, cross entropy error, root mean square error.

The authors are with University of San Carlos, Cebu City, 6000, Philippines (e-mail: janecolleenventura@gmail.com, racobosa@gmail.com, amceniza@usc.edu.ph, kris.capao@gmail.com).

[PDF]

Cite: Jane Colleen T. Ventura, R. B. Angelou F. Cobosa, Angie M. Ceniza, and Kris A. Capao, "Predicting the Typhoons in the Philippines Using Radial Basis Function Neural Network," International Journal of Modeling and Optimization vol. 7, no. 6, pp. 352-356, 2017.

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