Articles | Volume 20, issue 6
Research article
14 Jun 2016
Research article |  | 14 Jun 2016

Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Usman T. Khan and Caterina Valeo


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (20 Feb 2016) by Dimitri Solomatine
AR by Caterina Valeo on behalf of the Authors (29 Mar 2016)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (17 Apr 2016) by Dimitri Solomatine
RR by Anonymous Referee #1 (13 May 2016)
RR by Anonymous Referee #2 (23 May 2016)
ED: Publish as is (24 May 2016) by Dimitri Solomatine
ED: Publish as is (27 May 2016) by Dimitri Solomatine
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.