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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
environmental science
Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring
Environmental Science and Pollution Research, Volume 21, No. 3, Year 2014
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Description
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. © 2013 Springer-Verlag Berlin Heidelberg.
Authors & Co-Authors
Ahmed, Ali Najah
Malaysia, Terengganu,
Universiti Malaysia Terengganu
El-Shafie, Ahmed
Malaysia, Bangi
Universiti Kebangsaan Malaysia
Karim, O. A.
Malaysia, Bangi
Universiti Kebangsaan Malaysia
El-Shafie, Amr H.
Libya, Benghazi
University of Benghazi
Statistics
Citations: 110
Authors: 4
Affiliations: 3
Identifiers
Doi:
10.1007/s11356-013-2048-4
ISSN:
09441344
e-ISSN:
16147499
Research Areas
Environmental