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Water Res. 2008 Jan;42(1-2):296-306. Epub 2007 Jul 20.

Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery.

Author information

1
Department of Civil Engineering and Engineering Informatics, Chung Hua University, Hsinchu, Taiwan 30067, ROC. lichen@chu.edu.tw

Abstract

Parallel GEGA was constructed by incorporating grammatical evolution (GE) into the parallel genetic algorithm (GA) to improve reservoir water quality monitoring based on remote sensing images. A cruise was conducted to ground-truth chlorophyll-a (Chl-a) concentration longitudinally along the Feitsui Reservoir, the primary water supply for Taipei City in Taiwan. Empirical functions with multiple spectral parameters from the Landsat 7 Enhanced Thematic Mapper (ETM+) data were constructed. The GE, an evolutionary automatic programming type system, automatically discovers complex nonlinear mathematical relationships among observed Chl-a concentrations and remote-sensed imageries. A GA was used afterward with GE to optimize the appropriate function type. Various parallel subpopulations were processed to enhance search efficiency during the optimization procedure with GA. Compared with a traditional linear multiple regression (LMR), the performance of parallel GEGA was found to be better than that of the traditional LMR model with lower estimating errors.

PMID:
17692356
DOI:
10.1016/j.watres.2007.07.014
[Indexed for MEDLINE]

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