Supervised committee machine with artificial intelligence for prediction of fluoride concentration

The study introduces a supervised committee machine with artificial intelligence (SCMAI) method to predict fluoride in ground water of Maku, Iran. Ground water is the main source of drinking water for the area. Management of fluoride anomaly needs better prediction of fluoride concentration. However...

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Bibliographic Details
Published in:Journal of hydroinformatics 2013-10, Vol.15 (4), p.1474-1490
Main Authors: ATA ALLAH NADIRI, FIJANI, Elham, TSAI, Frank T.-C, ASGHAR ASGHARI MOGHADDAM
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Drinking water
Earth sciences
Earth, ocean, space
Exact sciences and technology
Factor analysis
Fluoridation
Fluorides
Fuzzy logic
Geochemistry
Geology
Groundwater
Hydrogeology
Hydrology. Hydrogeology
Mathematical models
Methods
Mineralogy
Neural networks
Parameter identification
Silicates
Water geochemistry
Water resources
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Summary:The study introduces a supervised committee machine with artificial intelligence (SCMAI) method to predict fluoride in ground water of Maku, Iran. Ground water is the main source of drinking water for the area. Management of fluoride anomaly needs better prediction of fluoride concentration. However, the complex hydrogeological characteristics cause difficulties to accurately predict fluoride concentration in basaltic formation, non-basaltic formation, and mixing zone. SCMAI predicts fluoride by a nonlinear combination of individual AI models through an artificial intelligent system. Factor analysis is used to identify effective fluoride-correlated hydrochemical parameters as input to AI models. Four AI models, Sugeno fuzzy logic, Mamdani fuzzy logic, artificial neural network (ANN), and neuro-fuzzy are employed to predict fluoride concentration. The results show that all of these models have similar fitting to the fluoride data in the Maku area, and do not predict well for samples in the mixing zone. The SCMAI employs an ANN model to re-predict the fluoride concentration based on the four AI model predictions. The result shows improvement to the CMAI method, a committee machine with the linear combination of AI model predictions. The results also show significant fitting improvement to individual AI models, especially for fluoride prediction in the mixing zone.
ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2013.008