Parameter identification of river water quality models using a genetic algorithm.
Water Sci Technol
; 69(4): 687-93, 2014.
Article
em En
| MEDLINE
| ID: mdl-24569265
ABSTRACT
For solving the multi-parameter identification problem of a river water quality model, analytical methods for solving a river water quality model and traditional optimization algorithms are very difficult to implement. A new parameter identification model based on a genetic algorithm (GA) coupled with finite difference method (FDM) was constructed for the determination of hydraulic and water quality parameters such as the longitudinal dispersion coefficient, the pollutant degradation coefficient, velocity, etc. In this model, GA is improved to promote convergence speed by adding the elite replacement operator after the mutation operator, and FDM is applied for unsteady flows. Moreover the influence of observation noise on identified parameters was discussed for the given model. The method was validated by two numerical cases (in steady and unsteady flows respectively) and one practical application. The computational results indicated that the model could give good identification precision results and showed good anti-noise abilities for water quality models when the noise level ≤10%.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Poluentes Químicos da Água
/
Poluição da Água
/
Rios
/
Modelos Teóricos
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Water Sci Technol
Assunto da revista:
SAUDE AMBIENTAL
/
TOXICOLOGIA
Ano de publicação:
2014
Tipo de documento:
Article