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
Related authors
Everett Snieder, Karen Abogadil, and Usman T. Khan
Hydrol. Earth Syst. Sci., 25, 2543–2566, https://doi.org/10.5194/hess-25-2543-2021, https://doi.org/10.5194/hess-25-2543-2021, 2021
Short summary
Short summary
Flow distributions are highly skewed, resulting in low prediction accuracy of high flows when using artificial neural networks for flood forecasting. We investigate the use of resampling and ensemble techniques to address the problem of skewed datasets to improve high flow prediction. The methods are implemented both independently and in combined, hybrid techniques. This research presents the first analysis of the effects of combining these methods on high flow prediction accuracy.
Everett Snieder, Karen Abogadil, and Usman T. Khan
Hydrol. Earth Syst. Sci., 25, 2543–2566, https://doi.org/10.5194/hess-25-2543-2021, https://doi.org/10.5194/hess-25-2543-2021, 2021
Short summary
Short summary
Flow distributions are highly skewed, resulting in low prediction accuracy of high flows when using artificial neural networks for flood forecasting. We investigate the use of resampling and ensemble techniques to address the problem of skewed datasets to improve high flow prediction. The methods are implemented both independently and in combined, hybrid techniques. This research presents the first analysis of the effects of combining these methods on high flow prediction accuracy.
Related subject area
Subject: Water Resources Management | Techniques and Approaches: Uncertainty analysis
Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System
Coupled effects of observation and parameter uncertainty on urban groundwater infrastructure decisions
Disentangling sources of future uncertainties for water management in sub-Saharan river basins
Possibilistic response surfaces: incorporating fuzzy thresholds into bottom-up flood vulnerability analysis
Future hot-spots for hydro-hazards in Great Britain: a probabilistic assessment
Evaluation of impacts of future climate change and water use scenarios on regional hydrology
Planning for climate change impacts on hydropower in the Far North
Describing the interannual variability of precipitation with the derived distribution approach: effects of record length and resolution
Projected changes in US rainfall erosivity
Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data
Assessment of precipitation and temperature data from CMIP3 global climate models for hydrologic simulation
Robust global sensitivity analysis of a river management model to assess nonlinear and interaction effects
Sensitivity and uncertainty in crop water footprint accounting: a case study for the Yellow River basin
Irrigation efficiency and water-policy implications for river basin resilience
On an improved sub-regional water resources management representation for integration into earth system models
Statistical analysis of error propagation from radar rainfall to hydrological models
The implications of climate change scenario selection for future streamflow projection in the Upper Colorado River Basin
Prioritization of water management under climate change and urbanization using multi-criteria decision making methods
Crop yields response to water pressures in the Ebro basin in Spain: risk and water policy implications
Gwyneth Matthews, Christopher Barnard, Hannah Cloke, Sarah L. Dance, Toni Jurlina, Cinzia Mazzetti, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 26, 2939–2968, https://doi.org/10.5194/hess-26-2939-2022, https://doi.org/10.5194/hess-26-2939-2022, 2022
Short summary
Short summary
The European Flood Awareness System creates flood forecasts for up to 15 d in the future for the whole of Europe which are made available to local authorities. These forecasts can be erroneous because the weather forecasts include errors or because the hydrological model used does not represent the flow in the rivers correctly. We found that, by using recent observations and a model trained with past observations and forecasts, the real-time forecast can be corrected, thus becoming more useful.
Marina R. L. Mautner, Laura Foglia, and Jonathan D. Herman
Hydrol. Earth Syst. Sci., 26, 1319–1340, https://doi.org/10.5194/hess-26-1319-2022, https://doi.org/10.5194/hess-26-1319-2022, 2022
Short summary
Short summary
Sensitivity analysis can be harnessed to evaluate effects of model uncertainties on planning outcomes. This study explores how observation and parameter uncertainty propagate through a hydrogeologic model to influence the ranking of decision alternatives. Using global sensitivity analysis and evaluation of aquifer management objectives, we evaluate how physical properties of the model and choice of observations for calibration can lead to variations in decision-relevant model outputs.
Alessandro Amaranto, Dinis Juizo, and Andrea Castelletti
Hydrol. Earth Syst. Sci., 26, 245–263, https://doi.org/10.5194/hess-26-245-2022, https://doi.org/10.5194/hess-26-245-2022, 2022
Short summary
Short summary
This study aims at designing water supply strategies that are robust against climate, social, and land use changes in a sub-Saharan river basin. We found that robustness analysis supports the discovery of policies enhancing the resilience of water resources systems, benefiting the agricultural, energy, and urban sectors. We show how energy sustainability is affected by water availability, while urban and irrigation resilience also depends on infrastructural interventions and land use changes.
Thibaut Lachaut and Amaury Tilmant
Hydrol. Earth Syst. Sci., 25, 6421–6435, https://doi.org/10.5194/hess-25-6421-2021, https://doi.org/10.5194/hess-25-6421-2021, 2021
Short summary
Short summary
Response surfaces are increasingly used to identify the hydroclimatic conditions leading to a water resources system's failure. Partitioning the surface usually requires performance thresholds that are not necessarily crisp. We propose a methodology that combines the inherent uncertainty of response surfaces with the ambiguity of performance thresholds. The proposed methodology is illustrated with a multireservoir system in Canada for which some performance thresholds are imprecise.
Lila Collet, Shaun Harrigan, Christel Prudhomme, Giuseppe Formetta, and Lindsay Beevers
Hydrol. Earth Syst. Sci., 22, 5387–5401, https://doi.org/10.5194/hess-22-5387-2018, https://doi.org/10.5194/hess-22-5387-2018, 2018
Short summary
Short summary
Floods and droughts cause significant damages and pose risks to lives worldwide. In a climate change context this work identifies hotspots across Great Britain, i.e. places expected to be impacted by an increase in floods and droughts. By the 2080s the western coast of England and Wales and northeastern Scotland would experience more floods in winter and droughts in autumn, with a higher increase in drought hazard, showing a need to adapt water management policies in light of climate change.
Seungwoo Chang, Wendy Graham, Jeffrey Geurink, Nisai Wanakule, and Tirusew Asefa
Hydrol. Earth Syst. Sci., 22, 4793–4813, https://doi.org/10.5194/hess-22-4793-2018, https://doi.org/10.5194/hess-22-4793-2018, 2018
Short summary
Short summary
It is important to understand potential impacts of climate change and human water use on streamflow and groundwater levels. This study used climate models with an integrated hydrologic model to project future streamflow and groundwater level in Tampa Bay for a variety of future water use scenarios. Impacts of different climate projections on streamflow were found to be much stronger than the impacts of different human water use scenarios, but both were significant for groundwater projection.
Jessica E. Cherry, Corrie Knapp, Sarah Trainor, Andrea J. Ray, Molly Tedesche, and Susan Walker
Hydrol. Earth Syst. Sci., 21, 133–151, https://doi.org/10.5194/hess-21-133-2017, https://doi.org/10.5194/hess-21-133-2017, 2017
Short summary
Short summary
We know that climate is changing quickly in the Far North (the Arctic and sub-Arctic). Hydropower continues to grow in this region because water resources are perceived to be plentiful. However, with changes in glacier extent and permafrost, and more extreme events, will those resources prove reliable into the future? This study amasses the evidence that quantitative hydrology modeling and uncertainty assessment have matured to the point where they should be used in water resource planning.
Claudio I. Meier, Jorge Sebastián Moraga, Geri Pranzini, and Peter Molnar
Hydrol. Earth Syst. Sci., 20, 4177–4190, https://doi.org/10.5194/hess-20-4177-2016, https://doi.org/10.5194/hess-20-4177-2016, 2016
Short summary
Short summary
We show that the derived distribution approach is able to characterize the interannual variability of precipitation much better than fitting a probabilistic model to annual rainfall totals, as long as continuously gauged data are available. The method is a useful tool for describing temporal changes in the distribution of annual rainfall, as it works for records as short as 5 years, and therefore does not require any stationarity assumption over long periods.
M. Biasutti and R. Seager
Hydrol. Earth Syst. Sci., 19, 2945–2961, https://doi.org/10.5194/hess-19-2945-2015, https://doi.org/10.5194/hess-19-2945-2015, 2015
Short summary
Short summary
We estimate future changes in US erosivity from the most recent ensemble projections of daily and monthly rainfall accumulation. The expectation of overall increase in erosivity is confirmed by these calculations, but a quantitative assessment is marred by large uncertainties. Specifically, the uncertainty in the method of estimation of erosivity is more consequential than that deriving from the spread in climate simulations, and leads to changes of uncertain sign in parts of the south.
M. C. Peel, R. Srikanthan, T. A. McMahon, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 1615–1639, https://doi.org/10.5194/hess-19-1615-2015, https://doi.org/10.5194/hess-19-1615-2015, 2015
Short summary
Short summary
We present a proof-of-concept approximation of within-GCM uncertainty using non-stationary stochastic replicates of monthly precipitation and temperature projections and investigate the impact of within-GCM uncertainty on projected runoff and reservoir yield. Amplification of within-GCM variability from precipitation to runoff to reservoir yield suggests climate change impact assessments ignoring within-GCM uncertainty would provide water resources managers with an unjustified sense of certainty
T. A. McMahon, M. C. Peel, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 361–377, https://doi.org/10.5194/hess-19-361-2015, https://doi.org/10.5194/hess-19-361-2015, 2015
Short summary
Short summary
Here we assess GCM performance from a hydrologic perspective. We identify five better performing CMIP3 GCMs that reproduce grid-scale climatological statistics of observed precipitation and temperature over global land regions for future hydrologic simulation. GCM performance in reproducing observed mean and standard deviation of annual precipitation, mean annual temperature and mean monthly precipitation and temperature was assessed and ranked, and five better performing GCMs were identified.
L. J. M. Peeters, G. M. Podger, T. Smith, T. Pickett, R. H. Bark, and S. M. Cuddy
Hydrol. Earth Syst. Sci., 18, 3777–3785, https://doi.org/10.5194/hess-18-3777-2014, https://doi.org/10.5194/hess-18-3777-2014, 2014
L. Zhuo, M. M. Mekonnen, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 18, 2219–2234, https://doi.org/10.5194/hess-18-2219-2014, https://doi.org/10.5194/hess-18-2219-2014, 2014
C. A. Scott, S. Vicuña, I. Blanco-Gutiérrez, F. Meza, and C. Varela-Ortega
Hydrol. Earth Syst. Sci., 18, 1339–1348, https://doi.org/10.5194/hess-18-1339-2014, https://doi.org/10.5194/hess-18-1339-2014, 2014
N. Voisin, H. Li, D. Ward, M. Huang, M. Wigmosta, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 3605–3622, https://doi.org/10.5194/hess-17-3605-2013, https://doi.org/10.5194/hess-17-3605-2013, 2013
D. Zhu, D. Z. Peng, and I. D. Cluckie
Hydrol. Earth Syst. Sci., 17, 1445–1453, https://doi.org/10.5194/hess-17-1445-2013, https://doi.org/10.5194/hess-17-1445-2013, 2013
B. L. Harding, A. W. Wood, and J. R. Prairie
Hydrol. Earth Syst. Sci., 16, 3989–4007, https://doi.org/10.5194/hess-16-3989-2012, https://doi.org/10.5194/hess-16-3989-2012, 2012
J.-S. Yang, E.-S. Chung, S.-U. Kim, and T.-W. Kim
Hydrol. Earth Syst. Sci., 16, 801–814, https://doi.org/10.5194/hess-16-801-2012, https://doi.org/10.5194/hess-16-801-2012, 2012
S. Quiroga, Z. Fernández-Haddad, and A. Iglesias
Hydrol. Earth Syst. Sci., 15, 505–518, https://doi.org/10.5194/hess-15-505-2011, https://doi.org/10.5194/hess-15-505-2011, 2011
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, https://doi.org/10.1177/0309133312444943, 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, https://doi.org/10.1002/jgrc.20361, 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, https://doi.org/10.1016/j.envsoft.2010.10.016, 2011.
Alvisi, S. and Franchini, M.: Grey neural networks for river stage forecasting with uncertainty, Phys. Chem. Earth, 42, 108–118, https://doi.org/10.1016/j.pce.2011.04.002, 2012.
Alvisi, S., Mascellani, G., Franchini, M., and Bárdossy, A.: Water level forecasting through fuzzy logic and artificial neural network approaches, Hydrol. Earth Syst. Sci., 10, 1–17, https://doi.org/10.5194/hess-10-1-2006, 2006.
Antanasijević, D., Pocajt, V., Perić–Grujić, A., and Ristić, M.: Modelling of dissolved oxygen in the Danube River using artificial neural networks and Monte Carlo Simulation uncertainty analysis, J. Hydrol., 519, 1895–1907, https://doi.org/10.1016/j.jhydrol.2014.10.009, 2014.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology.: Artificial Neural Networks in Hydrology. II: Hydrologic Applications, J. Hydrol. Eng., 5, 124–137, https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124), 2000.
Ay, M. and Kisi, O.: Modeling of dissolved oxygen concentration using different neural network techniques in Foundation Creek, El Paso County, Colorado, J. Environ. Eng.-ASCE, 138, 654–662, https://doi.org/10.1061/(ASCE)EE.1943-7870.0000511, 2011.
Bárdossy, A., Bogardi, I., and Duckstein, L.: Fuzzy regression in hydrology, Water Resour. Res., 26, 1497–1508, https://doi.org/10.1029/WR026i007p01497, 1990.
Bárdossy, A., Mascellani, G., and Franchini, M.: Fuzzy unit hydrograph, Water Resour. Res., 42, W02401, https://doi.org/10.1029/2004WR003751, 2006.
Betrie, G. D., Sadiq, R., Morin, K. A., and Tesfamariam, S.: Uncertainty quantification and integration of machine learning techniques for predicting acid rock drainage chemistry: A probability bounds approach, Sci. Total Environ., 490, 182–190, https://doi.org/10.1016/j.scitotenv.2014.04.125, 2014.
Bow River Basin Council – Profile of the Bow River Basin: http://wsow.brbc.ab.ca/index.php?option=com_content&view=article&id=259&Itemid=83, last access: 3 September 2015.
CCME – Canadian Council of Ministers of the Environment: Canadian water quality guidelines for the protection of aquatic life: Dissolved oxygen (freshwater), Canadian Council of Ministers of the Environment, Winnipeg, MB, Canada, 1–6, 1999.
Chang, F. J., Tsai, Y. H., Chen, P. A., Coynel, A., and Vachaud, G.: Modeling water quality in an urban river using hydrological factors–Data driven approaches, J. Environ. Manage., 151, 87–96, https://doi.org/10.1016/j.jenvman.2014.12.014, 2015.
Civanlar, M. R. and Trussell, H. J.: Constructing membership functions using statistical data, Fuzzy Sets Syst., 18, 1-13, https://doi.org/10.1016/0165-0114(86)90024-2, 1986.
Deng, Y., Sadiq, R., Jiang, W., and Tesfamariam, S.: Risk analysis in a linguistic environment: a fuzzy evidential reasoning-based approach, Expert Syst. Appl., 38, 15438–15446, https://doi.org/10.1016/j.eswa.2011.06.018, 2011.
Dorfman, R. and Jacoby, H. D.: An illustrative model of a river basin pollution control, in: Models for Managing Regional Water quality, edited by: Dorfman, R., Jacoby, H. D., and Thomas Jr., H. A., Harvard University Press, Cambridge, MA, USA, 84–141, https://doi.org/10.4159/harvard.9780674419216.c3, 1972.
Duan, Q., Sorooshian, S., and Gupta, V.: Effective and efficient global optimisation for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015–1031, https://doi.org/10.1029/91WR02985, 1992.
Dubois, D. and Prade, H.: Fuzzy sets and probability: misunderstandings, bridges and gaps, in: Proceedings of the Second IEEE International Conference on Fuzzy Systems, 28 March–1 April 1993, San Francisco, USA, 1059–1068, https://doi.org/10.1109/FUZZY.1993.327367, 1993.
Dubois, D. and Prade, H.: Possibility Theory and its Applications: Where do we stand?, in: Springer Handbook of Computational Intelligence, edited by: Kacprzyk, J. and Pedrycz, W., Springer-Verlag, Berlin, Heidelberg, 31–60, https://doi.org/10.1007/978-3-662-43505-2_3, 2015.
Dubois, D., Prade, H., and Sandri, S.: On possibility/probability transformations, in: Fuzzy logic, edited by: Lowen, R. and Roubens, M., Springer Netherlands, 103–112, https://doi.org/10.1007/978-94-011-2014-2_10, 1993.
Dubois, D., Foulloy, L., Mauris, G., and Prade, H.: Probability–possibility transformations, triangular fuzzy sets, and probabilistic inequalities, Reliab. Comput., 10, 273–297, https://doi.org/10.1023/B:REOM.0000032115.22510.b5, 2004.
Elshorbagy, A., Corzo, G., Srinivasulu, S., and Solomatine, D. P.: Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology – Part 1: Concepts and methodology, Hydrol. Earth Syst. Sci., 14, 1931–1941, https://doi.org/10.5194/hess-14-1931-2010, 2010.
Environment Canada: Wateroffice Hydrometric Station Meta Data, https://wateroffice.ec.gc.ca/station_metadata/stationList_e.html, last access: 3 September 2015.
Ferrero, A., Prioli, M., Salicone, S., and Vantaggi, B.: A 2-D Metrology-Sound Probability–Possibility Transformation, IEEE T. Instrum. Meas., 62, 982–990, https://doi.org/10.1109/TIM.2013.2246910, 2013.
Golder Associates Ltd.: Bow River Impact Study – Phase 1: Model Development and Calibration, City of Calgary Wastewater, Utilities and Environmental Protection, Calgary, Alberta, 134 pp., 2004.
Guyonnet, D., Bourgine, B., Dubois, D., Fargier, H., Come, B., and Chiles, J.-P.: Hybrid approach for addressing uncertainty in risk assessments, J. Environ. Eng.-ASCE, 129, 68–78, https://doi.org/10.1061/(ASCE)0733-9372(2003)129:1(68), 2003.
Hall, M. J.: Urban Hydrology, Elsevier Applied Science, Essex, England, 299 pp., 1984.
Hauer, F. R. and Hill, W. R.: Temperature, light and oxygen, in: Methods in stream ecology, edited by: Hauer, F. R. and Lamberti, G. A., Academic Press, San Diego, CA, USA, 103–117, https://doi.org/10.1016/B978-012332908-0.50007-3, 2007.
He, J. and Valeo, C.: Comparative study of ANNs versus parametric methods in rainfall frequency analysis, J. Hydrol. Eng., 14, 172–184, https://doi.org/10.1061/(ASCE)1084-0699(2009)14:2(172), 2009.
He, J., Chu, A., Ryan, M. C., Valeo, C., and Zaitlin, B.: Abiotic influences on dissolved oxygen in a riverine environment, Ecol. Eng., 37, 1804–1814, https://doi.org/10.1016/j.ecoleng.2011.06.022, 2011.
He, J., Ryan, M. C., and Valeo, C.: Changes in Water Quality Characteristics and Pollutant Sources along a Major River Basin in Canada, in: Environmental Management of River Basin Ecosystems, edited by: Ramkumar, M., Kumaraswamy, K., and Mohanraj, R., Springer International Publishing, Cham, Switzerland, 525–548, https://doi.org/10.1007/978-3-319-13425-3_25, 2015.
Heddam, S.: Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA, Environ. Technol., 35, 1650–1657, https://doi.org/10.1080/09593330.2013.878396, 2014.
Hornik, K., Stinchcombe, M., and White, H.: Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359–366, https://doi.org/10.1016/0893-6080(89)90020-8, 1989.
Huang, Y., Chen, X., Li, Y. P., Huang, G. H., and Liu, T.: A fuzzy-based simulation method for modelling hydrological processes under uncertainty, Hydrol. Process., 24, 3718–3732, https://doi.org/10.1002/hyp.7790, 2010.
Iwanyshyn, M., Ryan, M. C., and Chu, A.: Separation of physical loading from photosynthesis/respiration processes in rivers by mass balance, Sci. Total Environ., 390, 205–214, https://doi.org/10.1016/j.scitotenv.2007.09.038, 2008.
Jacquin, A. P.: Possibilistic uncertainty analysis of a conceptual model of snowmelt runoff, Hydrol. Earth Syst. Sci., 14, 1681–1695, https://doi.org/10.5194/hess-14-1681-2010, 2010.
Kannel, P. R., Lee, S., Lee, Y. S., Kanel, S. R., and Khan, S. P.: Application of water quality indices and dissolved oxygen as indicators for river water classification and urban impact assessment, Environ. Monit. Assess., 132, 93–110, https://doi.org/10.1007/s10661-006-9505-1, 2007.
Kasiviswanathan, K. S. and Sudheer, K. P.: Quantification of the predictive uncertainty of artificial neural network based river flow forecast models, Stoch. Environ. Res. Risk A., 27, 137–146, https://doi.org/10.1007/s00477-012-0600-2, 2013.
Kasiviswanathan, K. S., Cibin, R., Sudheer, K. P., and Chaubey, I.: Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations, J. Hydrol., 499, 275–288, https://doi.org/10.1016/j.jhydrol.2013.06.043, 2013.
Kaufmann, A. and Gupta, M. M.: Introduction to Fuzzy Arithmetic: Theory and Applications, Van Nostrand Reinhold Company, New York, NY, USA, 351 pp., 1985.
Khan, U. T. and Valeo, C.: Predicting Dissolved Oxygen Concentration in Urban Watersheds: A Comparison of Fuzzy Number Based and Bayesian Data-Driven Approaches, in: Proceedings of the International Conference on Marine and Freshwater Environments, 6–8 August 2014, St John's, Canada, 1–10, 2014a.
Khan, U. T. and Valeo, C.: Peak flow prediction using fuzzy linear regression: Case study of the Bow River, in: Proceedings of the International Conference on Marine and Freshwater Environments, 6–8 August 2014, St Johns, NFLD, Canada, 1–10, 2014b.
Khan, U. T. and Valeo, C.: A new fuzzy linear regression approach for dissolved oxygen prediction, Hydrolog. Sci. J., 60, 1096–1119, https://doi.org/10.1080/02626667.2014.900558, 2015.
Khan, U. T. and Valeo, C.: Short-term peak flow rate prediction and flood risk assessment using fuzzy linear regression, J. Environ. Inform., in press, 2016.
Khan, U. T., Valeo, C., and He, J.: Non-linear fuzzy-set based uncertainty propagation for improved DO prediction using multiple-linear regression, Stoch. Environ. Res. Risk A., 27, 599–616, https://doi.org/10.1007/s00477-012-0626-5, 2013.
Klir, G. J. and Parviz, B.: Probability-possibility transformations: a comparison, Int. J. Gen. Syst., 21, 291–310, https://doi.org/10.1080/03081079208945083, 1992.
Lane, R. J.: The Water Survey of Canada: Hydrometric Technician Career Development Program Lesson Package No. 10.1 – Principles of Discharge Measurement, available at: http://publications.gc.ca/site/eng/463990/publication.html (last access: 10 June 2016), 1999.
Maier, H. R., Jain, A., Dandy, G. C., and Sudheer, K. P.: Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions, Environ. Model. Softw., 25, 891–909, https://doi.org/10.1016/j.envsoft.2010.02.003, 2010.
Martin-Clouaire, R., Cazemier, D. R., and Lagacherie, P.: Representing and processing uncertain soil information for mapping soil hydrological properties, Comput. Electron. Agr., 29, 41–57, https://doi.org/10.1016/S0168-1699(00)00135-6, 2000.
Mauris, G.: A review of relationships between possibility and probability representations of uncertainty in measurement, IEEE T. Instrum. Meas., 62, 622–632, https://doi.org/10.1109/TIM.2012.2218057, 2013.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., and Veith, T. L.: Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007.
Mujumdar, P. P. and Ghosh, S.: Modeling GCM and scenario uncertainty using a possibilistic approach: Application to the Mahanadi River, India, Water Resour. Res., 44, W06407, https://doi.org/10.1029/2007WR006137, 2008.
Napolitano, G., Serinaldi, F., and See, L.: Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: an empirical examination, J. Hydrol., 406, 199–214, https://doi.org/10.1016/j.jhydrol.2011.06.015, 2011.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models: Part I. A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Neupane, A., Wu, P., Ghanbarpour, R., and Martin, N.: Bow River Phosphorus Management Plan: Water Quality Modeling Scenarios, Alberta Environment and Sustainable Resource Development, Edmonton, AB, Canada, 23 pp., 2014.
Niemczynowicz, J.: Urban hydrology and water management – present and future challenges, Urban Water, 1, 1–14, https://doi.org/10.1016/S1462-0758(99)00009-6, 1999.
Oussalah, M.: On the probability/possibility transformations: a comparative analysis, Int. J. Gen. Syst. 29, 671–718, https://doi.org/10.1080/03081070008960969, 2000.
Pogue, T. R. and Anderson, C. W.: Processes controlling dissolved oxygen and pH in the upper Willamette River and major tributaries, Oregon, 1994, Water Resources Investigations Report 95-4205, US Geological Survey, Portland, OR, USA, 77 pp., 1995.
Robinson, K. L., Valeo, C., Ryan, M. C., Chu, A., and Iwanyshyn, M.: Modelling aquatic vegetation and dissolved oxygen after a flood event in the Bow River, Alberta, Canada, Can. J. Civil Eng., 36, 492–503, https://doi.org/10.1139/L08-126, 2009.
Sadiq, R., Rodriguez, M. J., Imran, S. A., and Najjaran, H.: Communicating human health risks associated with disinfection by-products in drinking water supplies: a fuzzy-based approach, Stoch. Environ. Res. Risk A., 21, 341–353, https://doi.org/10.1007/s00477-006-0069-y, 2007.
Serrurier, M. and Prade, H.: An informational distance for estimating the faithfulness of a possibility distribution, viewed as a family of probability distributions, with respect to data, Int. J. Approx. Reason., 54, 919–933, https://doi.org/10.1016/j.ijar.2013.01.011, 2013.
Shimazaki, H. and Shinomoto, S.: A method for selecting the bin size of a time histogram, Neural Comput., 19, 1503–1527, https://doi.org/10.1162/neco.2007.19.6.1503, 2007.
Shrestha, D. L. and Solomatine, D. P.: Data-driven approaches for estimating uncertainty in rainfall-runoff modelling, Int. J. River Basin Manage., 6, 109–122, https://doi.org/10.1080/15715124.2008.9635341, 2008.
Singh, K. P., Basant, A., Malik, A., and Jain, G.: Artificial neural network modelling of the river water quality – a case study, Ecol. Model., 220, 888–895, https://doi.org/10.1016/j.ecolmodel.2009.01.004, 2009.
Solomantine, D. P. and Ostfeld, A.: Data-driven modelling: some past experiences and new approaches, J. Hydroinform., 10, 3–22, https://doi.org/10.2166/hydro.2008.015, 2008.
Solomatine, D. P., See, L. M., and Abrahart, R. J.: Data-driven modelling: concepts, approaches and experiences, in: Practical Hydroinformatics, edited by: Abrahart, R. J., See, L. M., and Solomatine, D. P., Springer, Berlin, Heidelberg, Germany, 17–30, https://doi.org/10.1007/978-3-540-79881-1_2, 2008.
Van Steenbergen, N., Ronsyn, J., and Willems, P.: A non-parametric data-based approach for probabilistic flood forecasting in support of uncertainty communication, Environ. Model. Softw., 33, 92–105, https://doi.org/10.1016/j.envsoft.2012.01.013, 2012.
Verhoest, N. E. C., De Baets, B., Mattia, F., Satalino, G., Lucau, C., and Defourny, P.: A possibilistic approach to soil moisture retrieval from ERS synthetic aperture radar backscattering under soil roughness uncertainty, Water Resour. Res., 43, W07435, https://doi.org/10.1029/2006WR005295, 2007.
Wen, X., Fang, J., Diao, M., and Zhang, C.: Artificial neural network modelling of dissolved oxygen in the Heihe River, Northwestern China, Environ. Monit. Assess., 185, 4361–4371, https://doi.org/10.1007/s10661-012-2874-8, 2013.
YSI Inc.: YSI 5200: A Multiparameter Monitor and Control Specifications, https://www.ysi.com/File_Library/Documents/Specification_Sheets/W45-01-5200A.pdf, last access: 3 September 2015.
Zadeh, L. A.: Fuzzy sets, Inform. Control, 8, 338–353, https://doi.org/10.1016/S0019-9958(65)90241-X, 1965.
Zadeh, L. A.: Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets Syst., 1, 3–28, https://doi.org/10.1016/0165-0114(78)90029-5, 1978.
Zhang, K.: Modeling uncertainty and variability in health risk assessment of contaminated sites, PhD thesis, Civil Engineering, University of Calgary, Canada, 290 pp., 2009.
Zhang, K. and Achari, G.: Correlations between uncertainty theories and their applications in uncertainty propagation, in: Safety, reliability and risk of structures, infrastructures and engineering systems, edited by: Furuta, H., Frangopol, D. M., and Shinozuka, M., Taylor and Francis Group, London, UK, 1337–1344, https://doi.org/10.1201/9781439847657-c20, 2010.
Zhang, K., Li, H., and Achari, G.: Fuzzy-stochastic characterisation of site uncertainty and variability in groundwater flow and contaminant transport through a heterogeneous aquifer, J. Contam. Hydrol., 106, 73–82, https://doi.org/10.1016/j.jconhyd.2009.01.003, 2009.
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....
