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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Cited articles

Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., Asaad Y. Shamseldin, A. Y., Solomatine, D. P., Toth, E., and Wilby, R. L.: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting, Prog. Phys. Geogr., 36, 480–513,, 2012.
Adams, K. A., Barth, J. A., and Chan, F.: Temporal variability of near-bottom dissolved oxygen during upwelling off central Oregon, J. Geophys. Res.-Oceans, 118, 4839–4854,, 2013.
AENV – Alberta Environment: Alberta water quality guideline for the protection of freshwater aquatic life: Dissolved oxygen, Catalogue #: ENV-0.94-OP, Standards and Guidelines Branch, Alberta Environment, Edmonton, Alberta, Canada, 42–56, 1997.
Alvisi, S. and Franchini, M.: Fuzzy neural networks for water level and discharge forecasting with uncertainty, Environ. Model. Softw., 26, 523–537,, 2011.
Alvisi, S. and Franchini, M.: Grey neural networks for river stage forecasting with uncertainty, Phys. Chem. Earth, 42, 108–118,, 2012.
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.