Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis
| dc.contributor.author | Khan, Usman T. | |
| dc.contributor.author | Valeo, Caterina | |
| dc.date.accessioned | 2018-08-27T13:54:47Z | |
| dc.date.available | 2018-08-27T13:54:47Z | |
| dc.date.copyright | 2017 | en_US |
| dc.date.issued | 2017 | |
| dc.description.abstract | A 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.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Faculty | en_US |
| dc.description.sponsorship | The 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.citation | Khan, 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/w9060381 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.3390/w9060381 | |
| dc.identifier.uri | http://hdl.handle.net/1828/9951 | |
| dc.language.iso | en | en_US |
| dc.publisher | Water | en_US |
| dc.subject | dissolved oxygen | |
| dc.subject | water quality | |
| dc.subject | artificial neural networks | |
| dc.subject | fuzzy numbers | |
| dc.subject | risk analysis | |
| dc.subject | uncertainty | |
| dc.subject.department | Department of Mechanical Engineering | |
| dc.title | Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis | en_US |
| dc.type | Article | en_US |