Title :
Applying Genetic Algorithm Combining Operation Tree (GAOT) for Estimating Salinity of Taiwan Strait Using MODIS/Terra
Author :
Alabbadi, Basmah Mohammad Mufleh ; Li Chen
Author_Institution :
Dept. of Civil Eng., Chung Hua Univ., Hsinchu, Taiwan
Abstract :
This paper proposes genetic algorithm combining operation tree (GAOT) and applies it to estimate the sea salinity of Taiwan Strait (TS) using MODIS/Terra data. GAOT is a data mining method, used to automatically discover the relationships among nonlinear systems. The main advantage of GAOT is to optimize appropriate types of function and their associated coefficients simultaneously. In the case study, this GAOT described above combining with MODIS/Terra seven bands was employed. These results are then verified with in situ sea salinity data of TS. The results show that the GAOT generates accurate multi-variable equation and has better performance than linear regression (LR) method.
Keywords :
data mining; genetic algorithms; geophysics computing; oceanographic regions; salinity (geophysical); trees (mathematics); GAOT; MODIS/Terra data; Taiwan Strait; data mining method; genetic algorithm combining operation tree; in situ sea salinity data; multivariable equation; nonlinear systems; sea salinity estimation; Genetic algorithms; MODIS; Mathematical model; Sea measurements; Sea surface salinity; MODIS/Terra; genetic algorithm combining operation tree (GAOT); linear regression; sea salinity;
Conference_Titel :
Intelligent Systems (GCIS), 2013 Fourth Global Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4799-2885-9
DOI :
10.1109/GCIS.2013.8