Predicting Sea Surface Salinity Using an Improved Genetic Algorithm Combining Operation Tree Method
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RESEARCH ARTICLE
Predicting Sea Surface Salinity Using an Improved Genetic Algorithm Combining Operation Tree Method Li Chen1 • Basmah Alabbadi1 • Chih-Hung Tan2 • Tai-Sheng Wang2 Kuo-Chang Li1
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Received: 27 February 2016 / Accepted: 11 October 2016 Ó Indian Society of Remote Sensing 2016
Abstract 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 & Li Chen [email protected] Basmah Alabbadi [email protected] Chih-Hung Tan [email protected] Tai-Sheng Wang [email protected] Kuo-Chang Li [email protected] 1
Department of Civil Engineering, Chung Hua University, Hsinchu 30012, Taiwan, ROC
2
Engineering Division, Agricultural Engineering Research Center, Taoyuan 32061, Taiwan, ROC
Imaging Spectroradiometer (MODIS) sensor. The results indicate that the IGAOT model performs more favorably than do the GAOT and linear regression (LR1 and LR2) 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. Keywords Improved genetic algorithm operation tree (IGAOT) Linear regression (LR1 and LR2) Moderateresolution imaging spectroradiometer (MODIS) Salinity Taiwan Strait
Introduction Now a days evolutionary computation techniques that are based on a powerful principle of evolution ‘‘survival of the fittest’’, are very efficient optimization methods. Types of well-known algorithms in this domain including genetic algorithms (GAs), genetic programming, evolutionary programming, neuroevolution, differential evolution, and evolution strategies. Among these methods, GA is the most popular one to solve a discrete optimization problem (Davis 1991). GA employs a unique search algorithm that can transfer from a local optimum to close to the global optimum. The main advantages of GA are nonlinearity, parallelism and flexibility (Goldberg 1989). A newly developed programming system, genetic algorithm combining operation tree (GAOT), has been used in some researches to find the best function and
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