Dissolved oxygen prediction using a possibility theory based fuzzy neural network
A new fuzzy neural network method to predict minimum dissolved oxygen (DO) concentration in a highly urbanised riverine environment (in Calgary, Canada) is proposed. The method uses abiotic factors (non-living, physical and chemical attributes) as inputs to the model, since the physical mechanisms g...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/20/2267/2016/hess-20-2267-2016.pdf |
| Summary: | A new fuzzy neural network method to predict minimum dissolved oxygen (DO)
concentration in a highly urbanised riverine environment (in Calgary, Canada)
is proposed. The method uses abiotic factors (non-living, physical and
chemical attributes) as inputs to the model, since the physical mechanisms
governing DO in the river are largely unknown. A new two-step method to
construct fuzzy numbers using observations is proposed. Then an existing
fuzzy neural network is modified to account for fuzzy number inputs and also
uses possibility theory based intervals to train the network. Results
demonstrate that the method is particularly well suited to predicting low DO
events in the Bow River. Model performance is compared with a fuzzy neural
network with crisp inputs, as well as with a traditional neural network.
Model output and a defuzzification technique are used to estimate the risk of
low DO so that water resource managers can implement strategies to prevent
the occurrence of low DO. |
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| ISSN: | 1027-5606 1607-7938 |