Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models
This paper describes the application of multilayer perceptron (MLP), radial basis network and adaptive neuro-fuzzy inference system (ANFIS) models for computing dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD) levels in the Karoon River (Iran). Nine input water...
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Veröffentlicht in: | International journal of environmental science and technology (Tehran) 2014-04, Vol.11 (3), p.645-656 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | This paper describes the application of multilayer perceptron (MLP),
radial basis network and adaptive neuro-fuzzy inference system (ANFIS)
models for computing dissolved oxygen (DO), biochemical oxygen demand
(BOD) and chemical oxygen demand (COD) levels in the Karoon River
(Iran). Nine input water quality variables including EC, PH, Ca, Mg,
Na, Turbidity, PO4, NO3 and NO2, which were measured in the river
water, were employed for the models. The performance of these models
was assessed by the coefficient of determination R2, root mean square
error and mean absolute error. The results showed that the computed
values of DO, BOD and COD using both the artificial neural network and
ANFIS models were in close agreement with their respective measured
values in the river water. MLP was also better than other models in
predicting water quality variables. Finally, the sensitive analysis was
done to determine the relative importance and contribution of the input
variables. The results showed that the phosphate was the most effective
parameters on DO, BOD and COD. |
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ISSN: | 1735-1472 1735-2630 |
DOI: | 10.1007/s13762-013-0378-x |