A Study of Applying Genetic Algorithm to Predict Reservoir Water Quality - Volume 7 Number 2 (Apr. 2017) - ijmo
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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(2): 98-104 ISSN: 2010-3697
DOI: 10.7763/IJMO.2017.V7.566

A Study of Applying Genetic Algorithm to Predict Reservoir Water Quality

L. Chen, M. Jamal, C. Tan, and B. Alabbadi
Abstract—This paper is aimed at demonstrating a genetic algorithm method and applying it to predict the water quality of reservoir in Taiwan island using remote sensing data. Genetic algorithms will be combined with operation tree (GAOT) to find the relationships between input and output data. A fittest function type will be obtained automatically from this method. The advantages of GA are global optimization, nonlinearity, flexibility and parallelism. In the current case study, GA is used to construct the relationship between algae concentration and Landsat sensor data. The results show that the model has better performance than the traditional LN transform of linear regression method, and similar performance compared with back-propagation neural network (BPNN) method.

Index Terms—Genetic algorithm, Landsat, LN transform linear regression, back-propagation neural network, operation tree.

Li Chen, Mohammad Jamal Mtlak Abualghanam, and Basmah Alabaddi are with the Department of Civil Engineering, Chung Hua University, Hsinchu 707, Taiwan, ROC (e-mail: lichen@chu.edu.tw, chtan@aerc.org.tw, basmaalabadi@yahoo.com).
Chih-Hung Tan is with the Department of Agricultural Engineering Research Center, No.196-1, Zhongyuan Rd., Zhongli Dist. Taoyuan City 32061, Taiwan.

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Cite: L. Chen, M. Jamal, C. Tan, and B. Alabbadi, "A Study of Applying Genetic Algorithm to Predict Reservoir Water Quality," International Journal of Modeling and Optimization vol. 7, no. 2, pp. 98-104, 2017.

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