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


Total article views: 2,136 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,174 859 103 2,136 63 47
  • HTML: 1,174
  • PDF: 859
  • XML: 103
  • Total: 2,136
  • BibTeX: 63
  • EndNote: 47
Views and downloads (calculated since 26 Nov 2015)
Cumulative views and downloads (calculated since 26 Nov 2015)


Latest update: 27 May 2023
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.