Quantification of the predictive uncertainty of artificial neural network based river flow forecast models

Stochastic Environmental Research and Risk Assessment

Volumn 27, Issue 1, 2013, Pages 137-146

  Kasiviswanathan, K S ^a   Sudheer, K P ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

Artificial neural network (ANN); Bootstrap technique; Hydrological processes; Non linear function; Taylor series

Indexed keywords

STATISTICAL METHODS; TAYLOR SERIES; UNCERTAINTY ANALYSIS;

BOOTSTRAP TECHNIQUE; FIRST ORDER PARTIAL DIFFERENTIAL EQUATIONS; FIRST-ORDER TAYLOR SERIES; HYDROLOGICAL PROCESS; NONLINEAR FUNCTIONS; PREDICTIVE UNCERTAINTY; QUANTITATIVE ASSESSMENTS; UNCERTAINTY ASSESSMENT;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; BOOTSTRAPPING; FLOW FIELD; FLOW MODELING; FORECASTING METHOD; NUMERICAL MODEL; PREDICTION; QUANTITATIVE ANALYSIS; RIVER FLOW; UNCERTAINTY ANALYSIS;

UNITED STATES; WHITE RIVER [UNITED STATES];

EID: 84871440798     PISSN: 14363240     EISSN: 14363259     Source Type: Journal    
DOI: 10.1007/s00477-012-0600-2     Document Type: Article

References (43)

1. Abrahart RJ, See LM, Dawson CW, Shamseldin AY, Wilby RL (2010) Nearly two decades of neural network hydrologic modeling. In: Sivakumar B, Berndtsson R (eds) Advances in data-based approaches for hydrologic modeling and forecasting. World Scientific Publishing, Hackensack, pp 267-346.
2. Aksoy H, Dahamsheh A (2009) Artificial neural network models for forecasting monthly precipitation in Jordan. Stoch Environ Res Risk Assess 23(7): 917-931.
3. Alvisi S, Franchini M (2011) Fuzzy neural networks for water level and discharge forecasting with uncertainty. Environ Modell Softw 26(4): 523-537.
4. Behzadian K, Kapelan Z (2009) Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks. Environ Modell Softw 24(4): 530-541.
5. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, New York.
6. Boucher MA, Laliberté JP, Anctil F (2010) An experiment on the evolution of an ensemble of neural networks for streamflow forecasting. Hydrol Earth Syst Sci 14: 603-612.
7. Bowden GJ, Dandy GC, Maier HR (2004a) Input determination for neural network models in water resources applications. Part 1-background and methodology. J Hydrol 301(1-4): 75-92.
8. Bowden GJ, Dandy GC, Maier HR (2004b) Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river. J Hydrol 301(1-4): 93-107.
9. Campolo M, Soldati A, Andreussi P (1999) Forecasting river flow rate during low flow periods using neural networks. Water Resour Res 35(11): 3547-3552.
10. Chryssolouris G, Lee M, Ramsey A (1996) Confidence interval prediction for neural network models. IEEE Trans Neural Netw 7(1): 229-232.
11. Dawson CW, Wilby R (1998) An artificial neural network approach to rainfall runoff modeling. Hydrol Sci J 43(1): 47-66.
12. Dawson CW, Brown M, Wilby R (2000) Inductive learning approaches to rainfall-runoff modelling. Int J Neural Syst 10: 43-57.
13. Ding A, He X (2003) Back propagation of pseudo-errors: neural networks that are adaptive to heterogeneous noise. IEEE Trans Neural Netw 14(2): 253-262.
14. Elshorbagy A, Corzo G, Srinivasulu S, Solomatine DP (2010a) 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.
15. Elshorbagy A, Corzo G, Srinivasulu S, Solomatine DP (2010b) Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology-part 2: application. Hydrol Earth Syst Sci 14: 1943-1961.
16. Firat M, Gungor M (2010) Monthly total sediment forecasting using adaptive neuro fuzzy inference system. Stoch Environ Res Risk Assess 24: 259-270.
17. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading.
18. Han DT, Kwong LiS (2007) Uncertainties in real-time flood forecasting with neural networks. Hydrol Process 21(2): 223-228. doi: 10. 1002/hyp. 6184.
19. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
20. Hsieh C (1993) Some potential applications of artificial neural networks in financial management. J Syst Manag 44(4): 12-15.
21. Khan MS, Coulibaly P (2006) Bayesian neural network for rainfall-runoff modeling. Water Resour Res 42: W07409. doi: 10. 1029/2005WR003971.
22. Khosravi A, Nahavandi S, Creighton D (2010) A prediction interval-based approach to determine optimal structures of neural network meta models. Expert Syst Appl 37(3): 2377-2387.
23. Kingston GB, Lambert MF, Maier HR (2005) Bayesian training of artificial neural network used for water resources modeling. Water Resour Res 41: W12409. doi: 10. 1029/2005WR004152.
24. Luk KC, Ball JE, Sharma A (2000) A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J Hydrol 227: 56-65.
25. MacKay KJC (1992) A practical Bayesian framework for backpropagation networks. Neural Comput 4: 448-472.
26. Maier HR, Ashu J, Graeme CD, Sudheer KP (2010) Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ Modell Softw 25(8): 891-909.
27. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: 1. A discussion of principles. J Hydrol 10: 282-290.
28. Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2005) Short-term flood forecasting with a neurofuzzy model. Water Resour Res 41: W04004. doi: 10. 1029/2004WR003562.
29. Nix D, Weigend A (1994) Estimating the mean and variance of the target probability distribution. In IEEE international conference on neural networks.
30. Papadopoulos G, Edwards P, Murray A (2001) Confidence estimation methods for neural networks: a practical comparison. IEEE Trans Neural Netw 12(6): 1278-1287.
31. Sajikumar N, Thandaveswara BS (1999) A non-linear rainfall-runoff model using an artificial neural network. J Hydrol 216: 32-35.
32. Schittkowski K (2002) EASY-FIT: a software system for data fitting in dynamic systems. Struct Multidiscip Optim 23: 153-169.
33. Sharma SK, Tiwari KN (2009) Bootstrap based artificial neural network (BANN) analysis for hierarchical prediction of monthly runoff in Upper Damodar Valley Catchment. J Hydrol 374: 209-222.
34. Shrestha R, Nestmann F (2009) Physically based and data driven models and propagation of input uncertainties in river flood prediction. J Hydrol Eng 1412: 1309-1319.
35. Shrestha DL, Solomatine DP (2006) Machine learning approaches for estimation of prediction interval for the model output. Neural Netw 19(2): 225-235.
36. Silverman D, Dracup JA (2000) Artificial neural networks and long-range precipitation in California. J Appl Meteorol 31(1): 57-66.
37. Sonmez R (2011) Range estimation of construction costs using neural networks with bootstrap prediction intervals. Expert Syst Appl 38(8): 9913-9917.
38. Srivastav RK, Sudheer KP, Chaubey I (2007) A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models. Water Resour Res 43: W10407. doi: 10. 1029/2006WR005352.
39. Sudheer KP (2005) Knowledge extraction from trained neural network river flow models. J Hydrol Eng 10(4): 264-269.
40. Sudheer KP, Gosain AK, Ramasastri KS (2002) A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 16(6): 1325-1330.
41. Thirumalaiah K, Deo MC (2000) Hydrological forecasting using neural networks. J Hydrol Eng 5(2): 180-189.
42. Tiwari MK, Chatterjee C (2010) Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs). J Hydrol 382(1-4): 20-33.
43. Zhang X, Liang F, Srinivasan R, van Liew M (2009) Estimating uncertainty of streamflow simulation using Bayesian neural networks. Water Resour Res 45: W02403. doi: 10. 1029/2008WR007030.