11. Hall J, Solomatine D (2008) A framework for uncertainty analysis in flood risk management
decisions. Int J River Basin Manag 6(2):85–98. https://doi.org/10.1080/15715124.2008.
9635339
12. Merz B, Thieken AH (2005) Separating natural and epistemic uncertainty in flood frequency
analysis. J Hydrol 309(1–4):114–132. https://doi.org/10.1016/j.jhydrol.2004.11.015
13. Goetzinger J, Bardossy A (2008) Generic error model for calibration and uncertainty estimation
of hydrological models. Water Resour Res 44:W00B07. https://doi.org/10.1029/
2007WR006691
14. Liu Y, Gupta HV (2007) Uncertainty in hydrologic modeling: toward an integrated data
assimilation framework. Water Resour Res 43(7):1–18. https://doi.org/10.1029/
2006WR005756
15. Quinonero-Candela J, Rasmussen CE, Sinz F, Bousquet O, Schölkopf B (2006) Evaluating
predictive uncertainty challenge. Machine learning challenges. Evaluating predictive uncertainty, visual object classification, and recognising tectual entailment. Springer, New York.
http://link.springer.com/10.1007%2F11736790_1. Accessed 2 Mar 2016, pp 1–27
16. Renard B, Kavetski D, Kuczera G, Thyer M, Franks SW (2010) Understanding predictive
uncertainty in hydrologic modeling: the challenge of identifying input and structural errors.
Water Resour Res 46(5):W05521. https://doi.org/10.1029/2009WR008328
17. Wagener T, Gupta HV (2005) Model identification for hydrological forecasting under uncertainty. Stoch Environ Res Risk Assess 19(6):378–387. https://doi.org/10.1007/s00477-0050006-5
18. Melchers RE (1999) Structural reliability analysis and prediction, 2nd edn. Wiley, New York
19. Abebe AJ, Solomatine DP, Venneker RGW (2000) Application of adaptive fuzzy rule-based
models for reconstruction of missing precipitation events. Hydrol Sci J 45(3):425–436
20. Bárdossy A, Bronstert A, Merz B (1995) 1-, 2- and 3-dimensional modeling of water movement
in the unsaturated soil matrix using a fuzzy approach. Adv Water Resour 18(4):237–251
21. Hundecha Y, Bardossy A, Theisen HW (2001) Development of a fuzzy logic-based rainfallrunoff model. Hydrol Sci J 46(3):363–376
22. Plate E, Shahzad K (2015) Uncertainty analysis of multi-model flood forecasts. Water 7
(12):6788–6809. https://doi.org/10.3390/w7126654
23. Xuan Y, Cluckie ID, Wang Y (2009) Uncertainty analysis of hydrological ensemble forecasts in
a distributed model utilising short-range rainfall prediction. Hydrol Earth Syst Sci 13
(3):293–303
24. Dogulu N, López López P, Solomatine DP, Weerts AH, Shrestha DL (2015) Estimation of
predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their
comparison on contrasting catchments. Hydrol Earth Syst Sci 19:3181–3201. https://doi.org/10.
5194/hess-19-3181-2015
25. Shrestha DL, Solomatine DP (2006) Machine learning approaches for estimation of prediction
interval for the model output. Neural Netw 19:225–235. https://doi.org/10.1016/j.neunet.2006.
01.012
26. Shrestha DL, Rodriguez J, Price RK, Solomatine DP (2006) Assessing model prediction limits
using fuzzy clustering and machine learning. Proceedings of the 7th international conference on
hydroinformatics, 4–8 September, Nice, France
27. Beven K, Binley A (2014) GLUE: 20 years on. Hydrol Process 28:5897–5918. https://doi.org/
10.1002/hyp.10082
28. Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty
prediction. Hydrol Process 6:279–298. https://doi.org/10.1002/hyp.3360060305
29. Shrestha DL, Kayastha N, Solomatine D (2009) A novel approach to parameter uncertainty
analysis of hydrological models using neural networks. Hydrol Earth Syst Sci 13:1235–1248
30. Solomatine D, Shrestha DL (2009) A novel method to estimate total model uncertainty using
machine learning techniques. Water Resour Res 45:W00B11. https://doi.org/10.1029/
2008WR006839
230
M. Mazzoleni et al.
decisions. Int J River Basin Manag 6(2):85–98. https://doi.org/10.1080/15715124.2008.
9635339
12. Merz B, Thieken AH (2005) Separating natural and epistemic uncertainty in flood frequency
analysis. J Hydrol 309(1–4):114–132. https://doi.org/10.1016/j.jhydrol.2004.11.015
13. Goetzinger J, Bardossy A (2008) Generic error model for calibration and uncertainty estimation
of hydrological models. Water Resour Res 44:W00B07. https://doi.org/10.1029/
2007WR006691
14. Liu Y, Gupta HV (2007) Uncertainty in hydrologic modeling: toward an integrated data
assimilation framework. Water Resour Res 43(7):1–18. https://doi.org/10.1029/
2006WR005756
15. Quinonero-Candela J, Rasmussen CE, Sinz F, Bousquet O, Schölkopf B (2006) Evaluating
predictive uncertainty challenge. Machine learning challenges. Evaluating predictive uncertainty, visual object classification, and recognising tectual entailment. Springer, New York.
http://link.springer.com/10.1007%2F11736790_1. Accessed 2 Mar 2016, pp 1–27
16. Renard B, Kavetski D, Kuczera G, Thyer M, Franks SW (2010) Understanding predictive
uncertainty in hydrologic modeling: the challenge of identifying input and structural errors.
Water Resour Res 46(5):W05521. https://doi.org/10.1029/2009WR008328
17. Wagener T, Gupta HV (2005) Model identification for hydrological forecasting under uncertainty. Stoch Environ Res Risk Assess 19(6):378–387. https://doi.org/10.1007/s00477-0050006-5
18. Melchers RE (1999) Structural reliability analysis and prediction, 2nd edn. Wiley, New York
19. Abebe AJ, Solomatine DP, Venneker RGW (2000) Application of adaptive fuzzy rule-based
models for reconstruction of missing precipitation events. Hydrol Sci J 45(3):425–436
20. Bárdossy A, Bronstert A, Merz B (1995) 1-, 2- and 3-dimensional modeling of water movement
in the unsaturated soil matrix using a fuzzy approach. Adv Water Resour 18(4):237–251
21. Hundecha Y, Bardossy A, Theisen HW (2001) Development of a fuzzy logic-based rainfallrunoff model. Hydrol Sci J 46(3):363–376
22. Plate E, Shahzad K (2015) Uncertainty analysis of multi-model flood forecasts. Water 7
(12):6788–6809. https://doi.org/10.3390/w7126654
23. Xuan Y, Cluckie ID, Wang Y (2009) Uncertainty analysis of hydrological ensemble forecasts in
a distributed model utilising short-range rainfall prediction. Hydrol Earth Syst Sci 13
(3):293–303
24. Dogulu N, López López P, Solomatine DP, Weerts AH, Shrestha DL (2015) Estimation of
predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their
comparison on contrasting catchments. Hydrol Earth Syst Sci 19:3181–3201. https://doi.org/10.
5194/hess-19-3181-2015
25. Shrestha DL, Solomatine DP (2006) Machine learning approaches for estimation of prediction
interval for the model output. Neural Netw 19:225–235. https://doi.org/10.1016/j.neunet.2006.
01.012
26. Shrestha DL, Rodriguez J, Price RK, Solomatine DP (2006) Assessing model prediction limits
using fuzzy clustering and machine learning. Proceedings of the 7th international conference on
hydroinformatics, 4–8 September, Nice, France
27. Beven K, Binley A (2014) GLUE: 20 years on. Hydrol Process 28:5897–5918. https://doi.org/
10.1002/hyp.10082
28. Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty
prediction. Hydrol Process 6:279–298. https://doi.org/10.1002/hyp.3360060305
29. Shrestha DL, Kayastha N, Solomatine D (2009) A novel approach to parameter uncertainty
analysis of hydrological models using neural networks. Hydrol Earth Syst Sci 13:1235–1248
30. Solomatine D, Shrestha DL (2009) A novel method to estimate total model uncertainty using
machine learning techniques. Water Resour Res 45:W00B11. https://doi.org/10.1029/
2008WR006839
230
M. Mazzoleni et al.