Predicting Sea Surface Salinity Using an Improved Genetic Algorithm Combining Operation Tree Method

The purpose of this study is to demonstrate the use of an improved genetic algorithm combining operation tree method (IGAOT) and apply it to monitor the salinity of the Taiwan Strait by using remote-sensing data. The genetic algorithm combining operation tree (GAOT) is a data mining method used to a...

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Bibliographic Details
Published in:Journal of the Indian Society of Remote Sensing 2017-08, Vol.45 (4), p.699-707
Main Authors: Chen, Li, Alabbadi, Basmah, Tan, Chih-Hung, Wang, Tai-Sheng, Li, Kuo-Chang
Format: Article
Language:English
Subjects:
Algorithms
Case studies
Correlation coefficients
Data mining
Dynamical systems
Earth and Environmental Science
Earth Sciences
Estimation
Genetic algorithms
MODIS
Nonlinear dynamics
Remote monitoring
Remote sensing
Remote Sensing/Photogrammetry
Research Article
Salinity
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Summary:The purpose of this study is to demonstrate the use of an improved genetic algorithm combining operation tree method (IGAOT) and apply it to monitor the salinity of the Taiwan Strait by using remote-sensing data. The genetic algorithm combining operation tree (GAOT) is a data mining method used to automatically discover relationships among nonlinear systems. Based on genetic algorithms (GAs), the relationships between input and output can be expressed as parse trees. The GAOT method typically has the disadvantages of premature convergence, which means it cannot produce satisfying solutions and performs satisfactorily when applied to only low-dimensional problems. Therefore, the GAOT method is enhanced using an automatic incremental procedure to improve the search ability of the method and avoid trapping in a local optimum. In this case study, an IGAOT is used to determine the relationship between the in situ data on the salinity of the Taiwan Strait and the data on the spectral parameters, seven wavebands, of a Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. The results indicate that the IGAOT model performs more favorably than do the GAOT and linear regression (LR 1 and LR 2 ) models, exhibits higher correlation coefficients, and involves fewer estimating errors. The results of this study indicate that the proposed technique is useful for estimating the Taiwan Strait salinity.
ISSN:0255-660X
0974-3006
DOI:10.1007/s12524-016-0637-7