Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring.
Environ Sci Pollut Res Int
; 21(3): 1658-1670, 2014 Feb.
Article
en En
| MEDLINE
| ID: mdl-23949111
ABSTRACT
We discuss the accuracy and performance of the adaptive neuro-fuzzy inference system (ANFIS) in training and prediction of dissolved oxygen (DO) concentrations. The model was used to analyze historical data generated through continuous monitoring of water quality parameters at several stations on the Johor River to predict DO concentrations. Four water quality parameters were selected for ANFIS modeling, including temperature, pH, nitrate (NO3) concentration, and ammoniacal nitrogen concentration (NH3-NL). Sensitivity analysis was performed to evaluate the effects of the input parameters. The inputs with the greatest effect were those related to oxygen content (NO3) or oxygen demand (NH3-NL). Temperature was the parameter with the least effect, whereas pH provided the lowest contribution to the proposed model. To evaluate the performance of the model, three statistical indices were used the coefficient of determination (R (2)), the mean absolute prediction error, and the correlation coefficient. The performance of the ANFIS model was compared with an artificial neural network model. The ANFIS model was capable of providing greater accuracy, particularly in the case of extreme events.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Oxígeno
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Contaminantes Químicos del Agua
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Contaminación Química del Agua
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Monitoreo del Ambiente
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Redes Neurales de la Computación
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Modelos Químicos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Environ Sci Pollut Res Int
Asunto de la revista:
SAUDE AMBIENTAL
/
TOXICOLOGIA
Año:
2014
Tipo del documento:
Article