Integrating a calibrated groundwater flow model with error-correcting data-driven models to improve predictions

Journal of Hydrology

Volumn 364, Issue 3-4, 2009, Pages 257-271

  Demissie, Yonas K ^a   Valocchi, Albert J ^a   Minsker, Barbara S ^a   Bailey, Barbara A ^b  

^a University of Illinois at Urbana Champaign   (United States)

^b UNIVERSITY OF SAN DIEGO   (United States)

Author keywords

Bias correction; Complementary modeling; Data driven model; Prediction error; Uncertainty

Indexed keywords

BIOREMEDIATION; ERROR CORRECTION; ERRORS; FLOW OF WATER; FORECASTING; GROUNDWATER; GROUNDWATER FLOW; HYDRAULICS; HYDROGEOLOGY; MEASUREMENT ERRORS; MODEL STRUCTURES; SYSTEMATIC ERRORS; UNCERTAINTY ANALYSIS; UNDERGROUND RESERVOIRS;

BIAS-CORRECTION; COMPLEMENTARY MODELING; DATA-DRIVEN MODEL; PREDICTION ERROR; UNCERTAINTY;

MATHEMATICAL MODELS;

CALIBRATION; FLOW MODELING; GROUNDWATER FLOW; NUMERICAL MODEL; PREDICTION; UNCERTAINTY ANALYSIS;

EID: 58149178536     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2008.11.007     Document Type: Article

References (71)

1. Abebe A.J., and Price R.K. Managing uncertainty in hydrological models using complementary models. Hydrological Sciences Journal 48 5 (2003) 679-692
2. Abebe A.J., and Price R.K. Information theory and neural networks for managing uncertainty in flood routing. Journal of Computing in Civil Engineering 18 4 (2004) 373-380
3. ASCE Task Committee. Artificial neural networks in hydrology, I: preliminary concepts; II: hydrological applications. Journal Hydrologic Engineering 5 2 (2000) 115-137
4. Asefa T., Kemblowski M.W., Urroz G., McKee M., and Khalil A. Support vectors-based groundwater head observation networks design. Water Resources Research 40 (2004) W11509-10 10.1029/2004WR003304
5. Babovic, V., Keijzer, M., Bundzel, M., 2000. Error correction in Venice lagoon using neural networks, support vector machines and local linear models. D2K Technical Report 0500-1, DHI, Netherlands.
6. Babovic, V., Fuhrman, D.R., 2001. Data assimilation and error prediction using local models. D2K Technical Report 0401-2, DHI, Netherlands.
7. Beven K.J., and Binley A. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 6 (1992) 279-298
8. Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. Classification and Regression Trees (1984), Wadsworth International Group, Belmont, CA 372pp
9. Carrera J., and Neuman S.P. Estimation of aquifer parameters under steady-state and transient condition: 1. Background and statistical framework. Water Resources Research 22 2 (1986) 199-210
10. Carrera J., Alcolea A., Medina A., Hidalgo J., and Slooten L.J. Inverse problem in hydrogeology. Hydrogeology Journal 13 (2005) 206-222
11. Certes C., and de Marsily G. Application of the pilot-points method to the identification of aquifer transmissivities. Advance Water Resources 14 5 (1991) 284-300
12. Cooley, R.L., 2004. A theory for modeling ground-water flow in heterogeneous media. US Geological Survey Professional Paper 1679, 220pp.
13. Cooley R.L., and Christensen S. Bias and uncertainty in regression-calibrated models of groundwater flow in heterogeneous media. Advances in Water Resources 29 5 (2006) 639-656
14. Deutsch C.V., and Journel A.G. GSLIB: geostatistical software library and user's guide (1998), Oxford University Press, New York 340pp
15. Dibike Y.B., Velickov S., Solomatine D., and Abbott M.B. Model induction with support vector machines: introduction and applications. Journal of Computing in Civil Engineering 15 3 (2001) 208-216
16. Doherty J. PEST: model-independent parameter estimation. fourth ed. (2002), Watermark Numerical Computing, Corinda, Australia 279pp
17. Doherty J. Groundwater model calibration using pilot points and regularization. Ground Water 41 2 (2003) 170-177
18. Draper R.N., and Smith H. Applied Regression Analysis (1998), John Wiley and Sons, New York 736pp
19. Durbin J., and Watson G.S. Testing for serial correlation in least-squares regression. Biometrika 58 (1971) 1-19
20. Franssen H.J.H., and Gómez-Hernández J.J. Impact of measurement errors in the sequential self-calibrated approach. Advances in Water Resources 26 5 (2003) 501-511
21. Gaganis P., and Smith L. Evaluation of the uncertainty of groundwater model predictions associated with conceptual errors: a perdatum approach to model calibration. Advances in Water Resources 29 4 (2006) 503-514
22. Gallagher M.R., and Doherty J. Parameter interdependence and uncertainty induced by lumping in a hydrologic model. Water Resources Research 43 (2007) W05421 10.1029/2006WR005347
23. Gómez-Hernández J.J., Sahuquillo A., and Capilla J.E. Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data: 1 - theory. Journal of Hydrology 204 1-4 (1997) 162-174
24. Goswami M., O'Connor K.M., Bhattarai K.P., and Shamseldin A.Y. Real-time river flow forecasting for the Brosna catchment in Ireland using eight updating models. Hydrology and Earth System Sciences 9 4 (2005) 394-411
25. Govindaraju R.S., and Rao A.R. Artificial Neural Networks in Hydrology (2000), Kluwer Academic Publishers, Amsterdam 351pp
26. Guadagnini A., and Neuman S.P. Nonlocal and localized analyses of conditional mean steady state flow in bounded, randomly nonuniform domains. 1. Theory and computational approach. Water Resources Research 35 10 (1999) 2999-3018
27. Gunn, S.R., 1998. Support vector machines for classification and regression, ISIS Tech. Report, University of Southampton, Southampton, UK, 66pp.
28. Gupta H.V., Sorooshian S., and Yapo P.O. Toward improved calibration of hydrologic models: multiple and noncommensurable measures of information. Water Resources Research 34 4 (1998) 751-763
29. Harbaugh, A.W., Banta, E.R., Hill, M.C., McDonald, M.G., 2000. MODFLOW-2000, the US geological survey modular ground-water model - user guide to modularization concepts and the ground-water flow process. Open-File Report 00-92, US Geological Survey, 121pp.
30. Hill, M.C., 1998. Methods and guidelines for effective model calibration, US Geological Survey. Water-Resources Investigations Report 98-4005, 97pp.
31. Hill M.C., and Tiedeman C.R. Effective groundwater model calibration, with analysis of sensitivities, predictions and uncertainty (2007), Wiley and Sons, New York 464pp
32. Højberg A.L., and Refsgaard J.C. Model uncertainty-parameter uncertainty versus conceptual models. Water Science and Technology 52 6 (2005) 177-186
33. Hornik K. Multilayer feed forward networks are universal approximators. Neural Networks 2 (1989) 359-366
34. Huard D., and Mailhot A. A Bayesian perspective on input uncertainty in model calibration: application to hydrological model ''abc". Water Resources Research 42 (2006) WR004661 10.1029/2005WR004661
35. Kavetski D., Franks S., and Kuczera G. Confronting input uncertainty in environmental modeling. In: Gupta H.V., Sorooshian S., Rousseau A.N., and Turcotte R. (Eds). Calibration of Watershed Models (2002), AGU Water Science and Applications Series 49-68
36. Khadam I., and Kaluarachchi J. Use of soft data to describe the relative uncertainty of calibration data in hydrologic models. Water Resources Research 40 11 (2004) W11505
37. Khu S.T., Liong S., Babovic V., Madsen H., and Muttil N. Genetic programming and its application in real-time flood forecasting. Journal of the American Water Resources Association 36 2 (2001) 439-452
38. Kitanidis P.K. The minimum-structure solution to the inverse problem. Water Resources Research 33 10 (1997) 2263-2272
39. Lundberg A. Combination of a conceptual model and an autoregressive error model for improving short time forecasting. Nordic Hydrology 13 4 (1982) 233-246
40. Madsen H. Parameter estimation on distributed hydrological catchment modeling using automatic calibration with multiple objectives. Advances in Water Resources 26 2 (2003) 205-216
41. Maier H.R., and Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environmental Modelling and Software 15 (2000) 101-124
42. McCulloch W.S., and Pitts W.H. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5 (1943) 115-133
43. McLaughlin D., and Townley L.R. A reassessment of the groundwater inverse problem. Water Resources Research 32 5 (1996) 1131-1161
44. Michael W.J., Minsker B.S., Tcheng D., and Valocchi A.J. Integrating data sources to improve hydraulic head predictions: a hierarchical machine learning approach. Water Resources Research 41 3 (2005) W03020 10.1029/2003WR002802
45. Mitchell M.T. Machine Learning (1997), McGraw Hill, New York 440pp
46. Moore C., and Doherty J. The role of the calibration process in reducing model predictive error. Water Resources Research 41 (2005) W05020 10.1029/2004WR003501
47. Moore C., and Doherty J. The cost of uniqueness in groundwater model calibration. Advance in Water Resources 29 4 (2006) 605-623
48. Mugunthan P., and Shoemaker C.A. Assessing the impacts of parameter uncertainty for computationally expensive groundwater models. Water Resources Research 42 (2006) W10428 10.1029/2005WR004640
49. Nash J.E., and Sutcliffe J.V. River flow forecasting through conceptual models part 1 - A discussion of principles. Journal of Hydrology 10 3 (1970) 282-290
50. Neuman S.P., and Yakowitz S. A statistical approach to the inverse problem of aquifer hydrology. 1. Theory. Water Resources Research 15 (1979) 845-860
51. Neuman S.P. Maximum likelihood Bayesian averaging of alternative conceptual-mathematical models. Stochastic Environmental Research and Risk Assessment 17 5 (2003) 291-305 10.1007/s00477-003-0151-7
52. Neuman, S.P., Wierenga, P.J., 2003. A comprehensive strategy of hydrogeologic modeling and uncertainty analysis for nuclear facilities and sites. NUREG/CR-6805, prepared for US Nuclear Regulatory Commission, Washington, DC, 309pp.
53. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufmann, California 302pp
54. Quinn J.J., Negri R.R., and Hinchman L.P. Predicting the effect of deep-rooted hybrid poplars on the groundwater flow system at a large-scale phytoremediation site. International Journal of Phytoremediation 3 1 (2001) 41-60
55. Ramarao B.S., LaVenue A.M., de Marsily G.H., and Marietta M.G. Pilot oint methodology for automated calibration of an ensemble of conditionally simulated transmissivity fields. 1-theory and computational experiments. Water Resources Research 31 3 (1995) 475-493
56. Refsgaard J.C., van der Sluijs J.P., Brown J., and van der Keur P. A framework for dealing with uncertainty due to model structure error. Advances in Water Resources 29 11 (2006) 1586-1597
57. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representation by error backpropagation. In: Rumelhart D.E., and McClelland J.L. (Eds). Parallel Distributed Processing (1986), MIT Press, Cambridge, MA
58. Sannasiraj S.A., Zhang H., Babovic V., and Chan E.S. Enhancing tidal prediction accuracy in a deterministic model using chaos theory. Advance in Water Resources 27 (2004) 761-772
59. Shamseldin A.Y. Application of a neural network technique to rainfall-runoff modeling. Journal of Hydrology 199 (1997) 272-294
60. Shamseldin A.Y., and O'Connor K.M. A nonlinear neural network technique for updating of river flow forecasts. Hydrology Earth System Sciences 5 4 (2001) 557-597
61. Solomatine D.P., and Dulal K.N. Model tree as an alternative to neural network in rainfall-runoff modeling. Hydrological Sciences Journal 48 3 (2003) 399-411
62. Spear R.C., Grieb T.M., and Shang N. Factor uncertainty and interaction in complex environmental models. Water Resources Research 30 (1994) 3159-3169
63. Tonkin M.J., and Doherty J. A hybrid regularized inversion methodology for highly parameterized environmental models. Water Resources Research 41 (2005) W10412 10.1029/2005WR003995
64. Tonkin M., Doherty J., and Moore C. Efficient nonlinear predictive error variance for highly parameterized models. Water Resources Research 43 (2007) W07429 10.1029/2006WR005348
65. Toth E., Montanari A., and Brath A. Real-time flood forecasting via combined use of conceptual and stochastic models. Physics Chemistry of the Earth 24 7 (1999) 793-798
66. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer-Verlag, New York 344pp
67. Vasco D.W., Peterson J.E., and Majer E.L. A simultaneous inversion of seismic travel times and amplitudes for velocity and attenuation. Geophysics 61 6 (1996) 1738-1757
68. Vrugt J.A., Diks C.G.H., Gupta H.V., Bouten W., and Verstraten J.M. Improved treatment of uncertainty in hydrologic modeling: combining the strengths of global optimization and data assimilation. Water Resources Research 41 (2005) W01017 10.1029/2004WR003059
69. Vrugt J.A., Gupta H.V., Bastidas W., Bouten W., and Sorooshian S. Effective and efficient algorithm for multi-objective optimization of hydrologic models. Water Resources Research 39 8 (2003) 1214
70. Yu P.S., Chen S.T., and Chang I.F. Support vector regression for real-time flood stage forecasting. Journal of Hydrology 328 3-4 (2006) 704-716
71. Zimmerman D.A., de Marsily G., Gotway C.A., Marietta M.G., Axness C.L., Beauheim R.L., Bras R.L., Carrera J., Dagan G., Davies P.B., Gallegos D.P., Galli A., Gómez-Hernández J.J., Grindrod P., Gutjahr A.L., Kitanidis P.K., LaVenue A.M., McLaughlin D., Neuman S.P., RamaRao B.S., Ravenne C., and Rubin Y. A comparison of seven geostatistically based inverse approaches to estimate transmissivities for modeling advective transport by groundwater flow. Water Resources Research 34 6 (1998) 1373-1413