Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Khan, Usman T.; Valeo, Caterina

doi:https://doi.org/10.5194/hess-20-2267-2016

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.

https://doi.org/10.5194/hess-20-2267-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

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

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Final revised paper (published on 14 Jun 2016)
Preprint (discussion started on 26 Nov 2015)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC C5798: 'Referee Comment', Anonymous Referee #1, 22 Dec 2015
- SC C6689: 'Response to Anonymous Referee #1', Usman Khan, 11 Feb 2016
RC C6049: 'Dissolved oxygen prediction using a', Anonymous Referee #2, 10 Jan 2016
- SC C6685: 'Response to Anonymous Referee #2', Usman Khan, 10 Feb 2016
AC C6816: 'Final Response to comments', Caterina Valeo, 18 Feb 2016

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.