Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis

dc.contributor.authorKhan, Usman T.
dc.contributor.authorValeo, Caterina
dc.date.accessioned2018-08-27T13:54:47Z
dc.date.available2018-08-27T13:54:47Z
dc.date.copyright2017en_US
dc.date.issued2017
dc.description.abstractA fuzzy neural network method is proposed to predict minimum daily dissolved oxygen concentration in the Bow River, in Calgary, Canada. Owing to the highly complex and uncertain physical system, a data-driven and fuzzy number based approach is preferred over traditional approaches. The inputs to the model are abiotic factors, namely water temperature and flow rate. An approach to select the optimum architecture of the neural network is proposed. The total uncertainty of the system is captured in the fuzzy numbers weights and biases of the neural network. Model predictions are compared to the traditional, non-fuzzy approach, which shows that the proposed method captures more low DO events. Model output is then used to quantify the risk of low DO for different conditions.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThe authors would also like to thank the agencies that funded this research: the Natural Sciences and Engineering Research Council of Canada (203098-2008); the Ministry of Advanced Education, Innovation and Technology-Government of British Columbia; and the University of Victoria. The authors would like to thank Dr S. Alvisi from the Universita degli Studi di Ferrara for providing the MATLAB code for the original Fuzzy Neural Network model. Lastly, we are grateful for the City of Calgary and Environment Canada for providing the data used in this research.en_US
dc.identifier.citationKhan, U.T. & Valeo, C. (2017). Optimising fuzzy neural network architecture for dissolved oxygen prediction and risk analysis. Water, 9(6), article 381. http://dx.doi.org/10.3390/w9060381en_US
dc.identifier.urihttp://dx.doi.org/10.3390/w9060381
dc.identifier.urihttp://hdl.handle.net/1828/9951
dc.language.isoenen_US
dc.publisherWateren_US
dc.subjectdissolved oxygenen_US
dc.subjectwater qualityen_US
dc.subjectartificial neural networksen_US
dc.subjectfuzzy numbersen_US
dc.subjectrisk analysisen_US
dc.subjectuncertaintyen_US
dc.titleOptimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysisen_US
dc.typeArticleen_US

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