Articles | Volume 20, issue 6
https://doi.org/10.5194/hess-20-2267-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-2267-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Dissolved oxygen prediction using a possibility theory based fuzzy neural network
Usman T. Khan
Mechanical Engineering, University of Victoria, P.O. Box 1700, Stn. CSC, Victoria, BC, V8W 2Y2, Canada
Mechanical Engineering, University of Victoria, P.O. Box 1700, Stn. CSC, Victoria, BC, V8W 2Y2, Canada
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Short summary
This paper contains a new two-step method to construct fuzzy numbers using observational data. In addition an existing fuzzy neural network is modified to account for fuzzy number inputs. This is combined with possibility-theory based intervals to train the network. Furthermore, model output and a defuzzification technique is used to estimate the risk of low Dissolved Oxygen so that water resource managers can implement strategies to prevent the occurrence of low Dissolved Oxygen.
This paper contains a new two-step method to construct fuzzy numbers using observational data....