Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models

References

• Burnash, R.J.C., Ferral, R.L., and McGuire, R.A., 1973. A generalized stream flow simulation system. In: Concerned modelling for digital computers. Sacramento, CA: Joint Federal–State River Forecast Centre.
   Google Scholar
• Chang, F.-J., Chiang, Y.-M., and Chang, L.-C., 2007. Multi-step-ahead neural networks for flood forecasting. Hydrological Sciences Journal, 52 (1), 114–130. doi:10.1623/hysj.52.1.114
   Web of Science ®Google Scholar
• Dawson, C.W., Abrahart, R.J., and See, L.M., 2007. HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environmental Modelling & Software, 22 (7), 1034–1052. doi:10.1016/j.envsoft.2006.06.008
   Web of Science ®Google Scholar
• Deb, K., et al., 2002. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6 (2), 182–197. doi:10.1109/4235.996017
   Web of Science ®Google Scholar
• Dhanesh, Y. and Sudheer, K.P., 2011. Predictions in ungauged basins: can we use artificial neural networks? American Geophysical Union Joint Assembly, Foz do Iguassu, Brazil, August 8–13, 2010.
   Google Scholar
• Ebtehaj, M., Moradkhani, H., and Gupta, H.V., 2010. Improving robustness of hydrologic parameter estimation by the use of moving block bootstrap resampling. Water Resources Research, 46, 1–14. doi:10.1029/2009WR007981
   Web of Science ®Google Scholar
• He, J., Anderson, A., and Valeo, C., 2015. Bias compensation in flood frequency analysis. Hydrological Sciences Journal, 60, 381–401. doi:10.1080/02626667.2014.885651
   Web of Science ®Google Scholar
• Hsu, K., Gupta, H.V., and Sorooshian, S., 1995. Artificial neural network modeling of the rainfall–runoff process. Water Resources Research, 31 (10), 2517–2530. doi:10.1029/95WR01955
   Web of Science ®Google Scholar
• Kasiviswanathan, K.S., et al., 2013. Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations. Journal of Hydrology, 499, 275–288. doi:10.1016/j.jhydrol.2013.06.043
   Web of Science ®Google Scholar
• Kavetski, D., Kuczera, G., and Franks, S.W., 2006. Bayesian analysis of input uncertainty in hydrological modeling: 2. Application. Water Resources Research, 42, W03408. doi:10.1029/2005WR004376
   Web of Science ®Google Scholar
• Kumar, A.R.S., et al., 2005. Rainfall-runoff modelling using artificial neural networks: comparison of network types. Hydrological Processes, 19 (6), 1277–1291. doi:10.1002/(ISSN)1099-1085
   Web of Science ®Google Scholar
• Kumar, M., Raghuwanshi, N.S., and Singh, R., 2002. Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering, 128, 224–233. doi:10.1061/(ASCE)0733-9437(2002)128:4(224)
   Web of Science ®Google Scholar
• Li, Y., et al., 2015. Parametric uncertainty and sensitivity analysis of hydrodynamic processes for a large shallow freshwater lake. Hydrological Sciences Journal, 60 (6), 1078–1095. doi:10.1080/02626667.2014.948444
   Web of Science ®Google Scholar
• Maier, H.R., et al., 2010. Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environmental Modelling & Software, 25, 891–909. doi:10.1016/j.envsoft.2010.02.003
   Web of Science ®Google Scholar
• Nayak, P.C., et al., 2005. Short-term flood forecasting with a neurofuzzy model. Water Resources Research, 41, 1–16. doi:10.1029/2004WR003562
   Web of Science ®Google Scholar
• Nestor, L.S.Y., 2006. Modelling the infiltration process with a multi-layer perceptron artificial neural network. Hydrological Sciences Journal, 51 (1), 3–20. doi:10.1623/hysj.51.1.3
   Web of Science ®Google Scholar
• Parasuraman, K. and Elshorbagy, A., 2008. Toward improving the reliability of hydrologic prediction: model structure uncertainty and its quantification using ensemble-based genetic programming framework. Water Resources Research, 44, 1–12. doi:10.1029/2007WR006451
   Web of Science ®Google Scholar
• Patil, A. and Deng, Z.-Q., 2012. Input data measurement induced uncertainty in watershed modelling. Hydrological Sciences Journal, 57 (1), 118–133. doi:10.1080/02626667.2011.636044
   Web of Science ®Google Scholar
• Sorooshian, S., Duan, Q., and Gupta, V.K., 1993. Calibration of rainfall–runoff models: application of global optimization to the Sacramento Soil Moisture Accounting model. Water Resources Research, 29, 1185–1194. doi:10.1029/92WR02617
   Web of Science ®Google Scholar
• Srivastav, R.K., Sudheer, K.P., and Chaubey, I., 2007. A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models. Water Resources Research, 43, 1–12. doi:10.1029/2006WR005352
   PubMed Web of Science ®Google Scholar
• Sudheer, K.P., Gosain, A.K., and Ramasastri, K.S., 2002. A data-driven algorithm for constructing artificial neural network rainfall–runoff models. Hydrological Processes, 16, 1325–1330. doi:10.1002/hyp.554
   Web of Science ®Google Scholar
• Tiwari, M.K., et al., 2013. Improving reliability of river flow forecasting using neural networks, wavelets and self-organising maps. Journal of Hydroinformatics, 15 (2), 486–502. doi:10.2166/hydro.2012.130
   Web of Science ®Google Scholar
• Tolson, B.A. and Shoemaker, C.A., 2007. Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resources Research, 43, W01413. doi:10.1029/2005WR004723
   Web of Science ®Google Scholar
• Vrugt, J.A., et al., 2003. A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resources Research, 39 (8), 1201. doi:10.1029/2002WR001642
   Web of Science ®Google Scholar
• Vrugt, J.A., et al., 2008. Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation. Water Resources Research, 44, 1–15. doi:10.1029/2007WR006720
   Web of Science ®Google Scholar
• Vrugt, J.A., et al., 2009. Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? Stochastic Environmental Research and Risk Assessment, 23, 1011–1026. doi:10.1007/s00477-008-0274-y
   Web of Science ®Google Scholar
• Ye, L., et al., 2014. Multi-objective optimization for construction of prediction interval of hydrological models based on ensemble simulations. Journal of Hydrology, 519, 925–933. doi:10.1016/j.jhydrol.2014.08.026
   Web of Science ®Google Scholar
• Yen, H., et al., 2015. Assessment of input uncertainty in SWAT using latent variables. Water Resources Management, 29, 1137–1153. doi:10.1007/s11269-014-0865-y
   Web of Science ®Google Scholar
• Zhang, X., et al., 2009. Estimating uncertainty of streamflow simulation using Bayesian neural networks. Water Resources Research, 45, 1–16. doi:10.1029/2008WR007030
   Web of Science ®Google Scholar
• Zhang, X., et al., 2011a. Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting. Journal of Hydrology, 409, 696–709. doi:10.1016/j.jhydrol.2011.09.002
   Web of Science ®Google Scholar
• Zhang, X.Y., et al., 2011b. Structural uncertainty assessment in a discharge simulation model. Hydrological Sciences Journal, 56 (5), 854–869. doi:10.1080/02626667.2011.587426
   Web of Science ®Google Scholar