Storm intensity estimation using symbolic aggregate approximation and artificial neural network

Author

Buranasing, Arthit ; Prayote, Akara

Author_Institution

Dept. of Comput. & Inf. Sci., King Mongkut´s Univ. of Technol. North Bangkok, Bangkok, Thailand

fYear

2014

fDate

July 30 2014-Aug. 1 2014

Firstpage

234

Lastpage

237

Abstract

A storm disaster is one of the most destructive natural hazards on earth and the main cause of death or injury to humans as well as damage or loss of valuable goods or properties, such as buildings, communication systems, agricultural land and etc. Storm intensity estimation is also important in evaluating the storm track prediction and risk area that will be affected by the storm. In this paper, proposed the storm intensity estimation model by using only 8 features to categorize major type of storm with symbolic aggregate approximation (SAX) and artificial neural network (ANN). The performance of the model is satisfactory, giving an average F-measure of 0.93 or 93%.

Keywords

approximation theory; disasters; geophysics computing; neural nets; storms; ANN; SAX; artificial neural network; natural hazards; storm disaster; storm intensity estimation; symbolic aggregate approximation; Artificial neural networks; Computational modeling; Estimation; Feature extraction; Predictive models; Storms; Tropical cyclones; artificial neural network (ANN); image processing; natural disasters; natural hazards; storm intensity prediction; symbolic aggregate approximation (SAX);

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Engineering Conference (ICSEC), 2014 International

Conference_Location

Khon Kaen

Print_ISBN

978-1-4799-4965-6

Type

conf

DOI

10.1109/ICSEC.2014.6978200

Filename

6978200