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.
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.
the Creative Commons Attribution 3.0 License.
Research article
| 
14 Jun 2016
Research article |
| 14 Jun 2016
Dissolved oxygen prediction using a possibility theory based fuzzy neural network
Usman T. Khan
Mechanical Engineering, University of Victoria, P.O. Box 1700, Stn. CSC, Victoria, BC, V8W 2Y2, Canada
Caterina Valeo
CORRESPONDING AUTHOR
Mechanical Engineering, University of Victoria, P.O. Box 1700, Stn. CSC, Victoria, BC, V8W 2Y2, Canada
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17 citations as recorded by crossref.
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- Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors S. Lee et al. 10.1007/s10040-018-1866-3
- Estimation of daily dissolved oxygen concentration for river water quality using conventional regression analysis, multivariate adaptive regression splines, and TreeNet techniques S. Nacar et al. 10.1007/s10661-020-08649-9
- Forecasting point-of-consumption chlorine residual in refugee settlements using ensembles of artificial neural networks M. De Santi et al. 10.1038/s41545-021-00125-2
- Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis U. Khan & C. Valeo 10.3390/w9060381
- Response of water quality to land use and sewage outfalls in different seasons J. Xu et al. 10.1016/j.scitotenv.2019.134014
- Simulation of Seawater Intrusion Area Using Feedforward Neural Network in Longkou, China D. Li et al. 10.3390/w12082107
- River flood prediction using fuzzy neural networks: an investigation on automated network architecture U. Khan et al. 10.2166/wst.2018.107
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- Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN A. Seifi et al. 10.3390/su12104023
- A Fuzzy-Based Framework for Assessing Uncertainty in Drift Prediction Using Observed Currents and Winds H. Blanken et al. 10.3389/fmars.2021.618094
- A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble H. Liu et al. 10.1016/j.eng.2020.10.023
- A Convolutional Neural Network Approach for Predicting Tunnel Liner Yield at Cigar Lake Mine J. Morgenroth et al. 10.1007/s00603-021-02563-3
- Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks F. Yang et al. 10.3390/su13179898
- The employment of polynomial chaos expansion approach for modeling dissolved oxygen concentration in river B. Keshtegar et al. 10.1007/s12665-018-8028-8
- Artificial Intelligence (AI) Studies in Water Resources M. Ay & S. Özyıldırım 10.28978/nesciences.424674
16 citations as recorded by crossref.
- A fuzzy entropy approach for design of hydrometric monitoring networks V. Sreeparvathy & V. Srinivas 10.1016/j.jhydrol.2020.124797
- Company employee quality evaluation model based on BP neural network T. Tseng et al. 10.3233/JIFS-189428
- Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors S. Lee et al. 10.1007/s10040-018-1866-3
- Estimation of daily dissolved oxygen concentration for river water quality using conventional regression analysis, multivariate adaptive regression splines, and TreeNet techniques S. Nacar et al. 10.1007/s10661-020-08649-9
- Forecasting point-of-consumption chlorine residual in refugee settlements using ensembles of artificial neural networks M. De Santi et al. 10.1038/s41545-021-00125-2
- Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis U. Khan & C. Valeo 10.3390/w9060381
- Response of water quality to land use and sewage outfalls in different seasons J. Xu et al. 10.1016/j.scitotenv.2019.134014
- Simulation of Seawater Intrusion Area Using Feedforward Neural Network in Longkou, China D. Li et al. 10.3390/w12082107
- River flood prediction using fuzzy neural networks: an investigation on automated network architecture U. Khan et al. 10.2166/wst.2018.107
- An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design J. Morgenroth et al. 10.3390/geosciences9120504
- Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN A. Seifi et al. 10.3390/su12104023
- A Fuzzy-Based Framework for Assessing Uncertainty in Drift Prediction Using Observed Currents and Winds H. Blanken et al. 10.3389/fmars.2021.618094
- A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble H. Liu et al. 10.1016/j.eng.2020.10.023
- A Convolutional Neural Network Approach for Predicting Tunnel Liner Yield at Cigar Lake Mine J. Morgenroth et al. 10.1007/s00603-021-02563-3
- Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks F. Yang et al. 10.3390/su13179898
- The employment of polynomial chaos expansion approach for modeling dissolved oxygen concentration in river B. Keshtegar et al. 10.1007/s12665-018-8028-8
1 citations as recorded by crossref.
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.
This paper contains a new two-step method to construct fuzzy numbers using observational data....
