Sensitivity analysis based on regional splits and regression trees (SARS-RT)

Environmental Modelling and Software

Volumn 21, Issue 7, 2006, Pages 976-990

  Pappenberger, Florian ^a   Iorgulescu, Ion ^b   Beven, Keith J ^a  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b EPFL   (Switzerland)

Author keywords

Calibration; Generalized likelihood uncertainty estimation; Random Forests; Regional sensitivity analysis; Regression tree; Sensitivity analysis; Uncertainty analysis

Indexed keywords

MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; REGIONAL PLANNING; REGRESSION ANALYSIS; TREES (MATHEMATICS);

GENERALIZED LIKELIHOOD UNCERTAINTY ESTIMATION; RANDOM FORESTS; REGIONAL SENSITIVITY ANALYSIS; REGRESSION TREE; UNCERTAINTY ANALYSIS;

SENSITIVITY ANALYSIS;

ERROR ANALYSIS; MULTIPLE REGRESSION; PARAMETERIZATION; SENSITIVITY ANALYSIS;

EID: 33646149763     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2005.04.010     Document Type: Article

References (72)

1. Babendreier J.E., and Castleton K.J. Investigating uncertainty and sensitivity in integrated, multimedia environmental models: tools for FRAMES-3MRA. Environmental Modelling & Software 20 8 (2005) 1043-1055
2. Beven K.J. Rainfall-Runoff Modelling: The Primer (2001), Wiley, New York
3. Beven K.J. Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system. Hydrological Processes 16 2 (2002) 189-206
4. Beven, K.J., A manifesto for the equifinality thesis. Journal of Hydrology, in press.
5. Beven K.J., and Binley A. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 6 (1992) 279-298
6. Beven, K.J., Pappenberger, F., 2003. Towards the hydraulics of the hydroinformatics era - by M.B. Abbott, V.M. Babovic and J.A. Cunge, Journal of Hydraulic Research, vol. 39. 2001, Issue 4, pp. 339-349 - discussion. Journal of Hydraulic Research, 41(3), 331-333.
7. Breiman L. Bagging predictors. Machine Learning 24 2 (1996) 123-140
8. Breiman L. Random forests. Machine Learning 45 1 (2001) 5-32
9. Breiman L. Statistical modeling: the two cultures. Statistical Science 16 (2001) 199-215
10. Breiman L., and Cutler A. Random Forests (2004).
11. Breiman L., Friedman J., Olshen R., and Stone C. Classification and Regression Trees (1984), Wadsworth Pub. Co.
12. Chan K., Saltelli A., and Tarantola A. Sensitivity analysis of model output: variance-based methods make the difference. In: Andradottir S., Healy K.J., Withers D.H., and Nelson B.L. (Eds). Winter Simulation Conference (1997).
13. Dietterich T.G. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Machine Learning 40 2 (2000) 139-157
14. Doherty J. PEST Model-Independent Parameter Estimation (2002).
15. Duan Q., Sorooshian S., and Gupta V. Effective and efficient global optimisation for conceptual rainfall-runoff models. Water Resources Research 28 (1992) 1015-1031
16. Esposito F., Malerba D., and Semeraro G. A comperative analysis of methods for pruning decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 476-491
17. Freund Y. An adaptive version of the boost by majority algorithm. Machine Learning 43 3 (2001) 293-318
18. Friedman J.H. A recursive partitioning decision rule for nonparametric classifiers. IEEE Transactions on Computers (1977) 404-408
19. Georgakakos, K.P., Krzysztofowicz, R., 2001. Probabilistic and ensemble forecasting. Journal of Hydrology 249 (1-4), 1-1.
20. Grieb T.M., Hudson R.J.M., Shang N., Spear R.C., Gherini S.A., and Goldstein R.A. Examination of model uncertainty and parameter interaction in a global carbon cycling model (GLOCO). Environment International 25 6-7 (1999) 787-803
21. 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
22. Hall J.W., Tarantola S., Bates P.D., and Horritt M.S. Distributed sensitivity analysis of flood inundation model calibration. ASCE Journal of Hydraulic Engineering 131 2 (2005) 117-126
23. Han D., Cluckie I.D., Karbassioun D., Lawry J., and Krauskopf B. River flow modelling using fuzzy decision trees. Water Resources Management 16 6 (2002) 431-445
24. Helton J.C., and Davis F.J. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering & System Safety 81 1 (2003) 23-69
25. Ho T.K. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 8 (1998) 832-844
26. Ho T.K. A data complexity analysis of comparative advantages of decision forest constructors. Pattern Analysis and Applications 5 2 (2002) 102-112
27. Hofer E. Sensitivity analysis in the context of uncertainty analysis for computationally intensive models. Computer Physics Communications 117 1-2 (1999) 21-34
28. Hornberger G.M., and Spear R.C. An approach to the preliminary analysis of environmental systems. Journal of Environmental Management 12 (1981) 7-18
29. Horritt M.S., and Bates P.D. Evaluation of 1D and 2D numerical models for predicting river flood inundation. Journal of Hydrology 268 1-4 (2003) 87-99
30. Iorgulescu I., and Beven K.J. Nonparametric direct mapping of rainfall-runoff relationships: an alternative approach to data analysis and modeling?. Water Resource Research 40 (2004) 10.1029/2004WR003094
31. Jolma A., and Norton J. Methods of uncertainty treatment in environmental models. Environmental Modelling & Software 20 8 (2005) 979-980
32. Karalic A., and Cestnik B. The Bayesian approach to tree-structured regression. Proceedings of the ITI-91 (1991) Cavtat, Croatia
33. Kavetski D.N., Franks S.W., and Kuczera G. Confronting input uncertainty in environmental modelling. In: Duan Q., Gupta H., Sorooshian S., Rousseau A.N., and Turcotte R. (Eds). Advances in Calibration of Watershed Models (2003), American Geophysical Union, Washington 49-68
34. Krzysztofowicz R. The case for probabilistic forecasting in hydrology. Journal of Hydrology 249 1-4 (2001) 2-9
35. Krzysztofowicz R. Bayesian system for probabilistic river stage forecasting. Journal of Hydrology 268 1-4 (2002) 16-40
36. Krzysztofowicz R. Probabilistic flood forecast: bounds and approximations. Journal of Hydrology 268 1-4 (2002) 41-55
37. Krzysztofowicz R., and Herr H.D. Hydrologic uncertainty processor for probabilistic river stage forecasting: precipitation-dependent model. Journal of Hydrology 249 1-4 (2001) 46-68
38. Kuczera G., and Parent E. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. Journal of Hydrology 211 1-4 (1998) 69-85
39. Matgen P., Henry J.-B.F., Pappenberger F., Pfister L., de Fraipont P., and Hoffmann L. Uncertainty in calibrating flood propagation models with flood boundaries derived from synthetic aperture radar imagery. XXth ISPRS Congress (2004), International Society for Photogrammetry and Remote Sensing, Istanbul, Turkey
40. McCuen R.H. Component sensitivity: a tool for the analysis of complex water resources systems. Water Resources Research 9 1 (1973) 243-247
41. McCuen R.H. The role of sensitivity analysis in hydrologic modelling. Journal of Hydrology 18 (1973) 37-53
42. Melching C.S. Reliability estimation. In: Singh V.P. (Ed). Computer Models of Watershed Hydrology (1995), Water Resources Publications, Littleton 69-118
43. Murthy, S.K., 1995. On Growing Better Decision Trees from Data, PhD thesis, Johns Hopkins University Baltimore, .
44. Oakley J.E., and O'Hagan A. Probabilistic sensitivity analysis of complex models: a Bayesian approach. Journal of the Royal Statistical Society Series B - Statistical Methodology 66 (2004) 751-769
45. Pallottino S., Sechi G.M., and Zuddas P. A DSS for water resources management under uncertainty by scenario analysis. Environmental Modelling & Software 20 8 (2005) 1031-1042
46. Pappenberger F. Uncertainty in Flood Inundation Models (2004), Lancaster University, Lancaster 325 pp.
47. Pappenberger, F., Beven, K., Horritt, M., Bates, P., 2003. Comparison of the uncertainty in ISIS and HEC-RAS within the Generalized Likelihood Uncertainty Estimation framework conditioned on historical data, EGS XXVII General Assembly, Nice, France.
48. Pappenberger F., Beven K.J., Horritt M., and Blazkova S. Uncertainty in the calibration of effective roughness parameters in HEC-RAS using inundation and downstream level observations. Journal of Hydrology 302 1-4 (2005) 46-69
49. Pappenberger, F., Matgen, P., Beven, K.J., Henry, J.-B.F., Pfister, L., de Fraipont, P., 2004. The influence of rating curve uncertainty on flood inundation predictions, Flood Risk Assessment, Bath.
50. Pastres R., Chan K., Solidoro C., and Dejak C. Global sensitivity analysis of a shallow-water 3D eutrophication model. Computer Physics Communications 117 1-2 (1999) 62-74
51. Pastres R., and Ciavatta S. A comparison between the uncertainties in model parameters and in forcing functions: its application to a 3D water-quality model. Environmental Modelling & Software 20 8 (2005) 981-989
52. Press W.H., Teukolsky S.A., Vetterling W.T., and Flannery B.P. Numerical Recipies in C++ (2002), Cambridge University Press, Cambridge
53. Quinlan J.R. Learning with continuous classes. Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence (1992), World Scientific 343-348
54. Ratto, M., Saltelli, A., Tarantola, A., Young, P. Improved and accelerated sensitivity analysis using state dependent parameter models, in press.
55. Ratto M., Tarantola A., and Saltelli A. Sensitivity analysis in model calibration: GSA-GLUE approach. Computer Physics Communications 136 (2001) 212-224
56. Reichert P., and Borsuk M.E. Does high forecast uncertainty preclude effective decision support?. Environmental Modelling & Software 20 8 (2005) 991-1001
57. Romanowicz R., and Beven K.J. Estimation of flood inundation probabilities as conditioned on event inundation maps. Water Resources Research 39 3 (2003) 10.1029/2001WR001056
58. Saltelli A., Tarantola A., Campolongo F., and Ratto M. Sensitivity Analysis in Practice - A Guide to Assessing Scientific Models (2004), John Wiley & Sons, Chichester
59. Segal, M.R., 2004. Machine Learning Benchmarks and Random Forest Regression, Center for Bioinformatics & Molecular Biostatistics.
60. Seibert J., and McDonnell J. The quest for an improved dialog between modeler and experimentalist. In: Duan Q., Gupta H., Sorooshian S., Rousseau A.N., and Turcotte R. (Eds). Advances in Calibration of Watershed Models (2003), American Geophysical Union, Washington
61. Sobol I.M. Sensitivity estimates for nonlinear mathematical models. Mathematical Modelling and Computational Experiment 1 (1993) 407-414
62. Spear R.C., Grieb T.M., and Shang N. Parameter Uncertainty and Interaction in Complex Environmental Models. Water Resources Research 30 11 (1994) 3159-3169
63. Spear R.C., and Hornberger G.M. Eutrophication in Peel Inlet, II. Identification of critical uncertainties via generalised sensitivity analysis. Water Resource Research 14 (1980) 43-49
64. Su X., Wang M., and Fan J. Maximum likelihood regression trees. Journal of Computational and Graphical Statistics 13 3 (2004) 586-598
65. Tarantola, A., Saltelli, A., 2004. The Simlab software package.
66. Torgo, L., 1999a. Inductive Learning of Tree-based Regression Models, University of Porto, Porto, .
67. Torgo, L., 1999b. Reliable Error Estimates for Pruning Regression Trees, University of Porto, Porto, .
68. U.S. Army Corps Engineers, HEC-RAS, http://www.hec.usace.army.mi.
69. Wagener T., McIntyre N., Lees M.J., Wheater H.S., and Gupta H.V. Towards reduced uncertainty in conceptual rainfall-runoff modelling: dynamic identifiability analysis. Hydrological Processes 17 2 (2003) 455-476
70. Wiles J., and Levine N. A combined GIS and HEC model for the analysis of the effect of urbanization on flooding: the Swan Creek watershed, Ohio. Environmental & Engineering Geosience 8 1 (2002) 47-61
71. Yapo P.O., Gupta H.V., and Sorooshian S. Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. Journal of Hydrology 181 1-4 (1996) 23-48
72. Yapo P.O., Gupta H.V., and Sorooshian S. Multi-objective global optimization for hydrologic models. Journal of Hydrology 204 1-4 (1998) 83-97