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

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 Feits...

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Bibliographic Details
Published in:Water research (Oxford) 2008, Vol.42 (1), p.296-306
Main Authors: Chen, Li, Tan, Chih-Hung, Kao, Shuh-Ji, Wang, Tai-Sheng
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
case studies
chlorophyll
Chlorophyll - analysis
Chlorophyll- a
environmental monitoring
Environmental Monitoring - methods
estimation
Exact sciences and technology
Grammatical evolution
image analysis
Landsat
mathematical models
Models, Statistical
optimization
Other industrial wastes. Sewage sludge
Parallel genetic algorithm
Pollution
regression analysis
remote sensing
Remote-sensed imagery
reservoirs
Satellite Communications
satellites
subtropics
Taiwan
Tropical Climate
Wastes
Water Pollution - analysis
water quality
Water quality monitoring
Water Supply - analysis
Water treatment and pollution
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Summary: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.
ISSN:0043-1354
1879-2448
DOI:10.1016/j.watres.2007.07.014