Incorporating multiple observations for distributed hydrologic model calibration: An approach using a multi-objective evolutionary algorithm and clustering

Advances in Water Resources

Volumn 31, Issue 10, 2008, Pages 1387-1398

  Khu, Soon Thiam ^a   Madsen, Henrik ^b   di Pierro, Francesco ^a  

^a UNIVERSITY OF EXETER   (United Kingdom)

^b DHI   (Denmark)

Author keywords

Calibration; Distributed modelling; Evolutionary algorithms; Groundwater; Multi objective; Multiple observations; Self organising map (SOM)

Indexed keywords

AGGLOMERATION; BOOLEAN FUNCTIONS; CATCHMENTS; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; EVOLUTIONARY ALGORITHMS; FLOW OF SOLIDS; GROUNDWATER; HYDRAULIC MODELS; HYDROGEOLOGY; MULTIOBJECTIVE OPTIMIZATION; PARAMETER ESTIMATION; RUNOFF; SET THEORY;

CALIBRATION; DISTRIBUTED MODELLING; MULTI-OBJECTIVE; MULTIPLE OBSERVATIONS; SELF-ORGANISING MAP (SOM);

MATHEMATICAL MODELS;

ALGORITHM; CALIBRATION; CATCHMENT; CLUSTER ANALYSIS; GROUNDWATER; HYDROLOGICAL MODELING; HYDROLOGICAL RESPONSE; MAPPING; OPTIMIZATION; PARAMETERIZATION; SELF ORGANIZATION;

EID: 51549110733     PISSN: 03091708     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advwatres.2008.07.011     Document Type: Article

References (50)

1. ASCE Task Committee. Definition of criteria of evaluation of watershed models. J Irrig Drain Eng, ASCE 1993;119(3):429-42.
2. Beven K., and Binley A.M. The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6 (1992) 279-298
3. Bogena H., Kunkel R., Schobel T., Schrey H.P., and Wendland F. Distributed modelling of groundwater recharge at the macroscale. Ecol Model 187 (2005) 15-26
4. Boyle D.P., Gupta H.V., and Sorooshian S. Toward improved calibration of hydrologic models: combining the strengths of manual and automatic methods. Water Resour Res 36 12 (2000) 3663-3674
5. Cheng C.-T., Zhao M., Chau K.W., and Wu X. Using genetic algorithm and TOPSIS for Xinanjing model calibration with a single procedure. J Hydrol 316 1-4 (2006) 129-140
6. Cheng C.-T., Wu X., and Chau K.W. Multiple criteria rainfall-runoff model calibration using a parallel genetic algorithm in a cluster of computer. Hydrol Sci J 50 6 (2005) 1069-1088
7. Cheng C.-T., Oh C., and Chau K.W. Combining a fuzzy optimal model with a genetic algorithm to solve multiobjective rainfall-runoff model calibration. J Hydrol 268 1-4 (2002) 72-86
8. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. Report No. 200001. Indian Institute of Technology, Kanpur Genetic Algorithms Laboratory; 2000. p. 20.
9. Dornes PF. Calibration strategy for parameter estimation in the MIKE SHE modelling system. Master of Science thesis. International Institute for Infrastructural, Hydraulic and Environmental Engineering, IHE, DELFT, The Netherlands.
10. Duan Q.Y., Sorooshian S., and Gupta V. Effective and efficient global optimisation for conceptual rainfall-runoff models. Water Resour Res 28 4 (1992) 1015-1031
11. Fonseca C.M., and Fleming P.J. Multiobjective optimisation. Handbook of evolutionary computation (1995), Oxford University Press, IOP Publishing Ltd.
12. Graham DN, Butts MB. Flexible, integrated watershed modelling with MIKE SHE. In: Singh VP, Frevert DK, editors. Watershed models, 2006. p. 245-72.
13. van Griensven A., and Bauwens W. Multiobjective autocalibration for semidistributed water quality models. Water Resour Res 39 12 (2005) 1348 10.1029/2003WR002284
14. Gupta H.V., Sorooshian S., and Yapo P.O. Toward improved calibration of hydrological models: multiple and non-commensurable measures of information. Water Resour Res 34 4 (1998) 751-763
15. Haykin S. Neural networks: a comprehensive foundation (1999), Prentice Hall
16. Henriksen H.J., Troldborg L., Nyegaard P., Sonnenborg T.O., Refsgaard J.C., and Madsen B. Methodology for construction, calibration and validation of a national hydrological model for Denmark. J Hydrol 280 1-4 (2003) 52-71
17. Horritt M. Calibration of a two-dimensional finite element flood flow model using satellite imagery. Water Resour Res 36 11 (2000) 3279-3291
18. Hunter N.M., Bates P., Horritt M., de Roo A., and Werner M. Utility of different data types for calibrating flood inundation models within a GLUE framework. Hydrol Earth Syst Sci 9 4 (2005) 412-430
19. Khadam I.M., and Kaluarachchi J.J. Use of soft information to describe the relative uncertainty of calibration data in hydrologic models. Water Resour Res 40 (2004) W11505 10.1029/2003WR002939
20. Khare V, Yao X, Deb K. Performance scaling of multi-objective evolutionary algorithms. In: Fonseca Carlos M, Fleming Peter J, Zitzler Eckart, Deb Kalyanmoy, Thiele Lothar, editors. Evolutionary multi-criterion optimization. Second international conference, EMO 2003, Lecture notes in computer science, vol. 2632. Faro, Portugal: Springer; 2003. p. 376-90.
21. Khu S.-T., diPierro F., Savic D., Djordjevic S., and Walters G.A. Incorporating spatial and temporal information for urban drainage model calibration: an approach using preference ordering genetic algorithm. Adv Water Resour 29 8 (2006) 10.1016/j.advwatres.2005.09.009
22. Khu S.-T., and Madsen H. Multiobjective calibration with Pareto preference ordering: an application to rainfall-runoff model calibration. Water Resour Res 41 3 (2005) 10.1029/2004WR003041
23. Khu S.-T., and Keedwell E. Introducing choices (flexibility) in upgrading of water distribution network: The New York City tunnel network example. Eng Opt 37 3 (2005) 291-305
24. Knowles J, Watson A, Corne D. Reducing local optima in single-objective problems by multiobjectivization. In: 1st International conference on evolutionary multi-criterion optimization, 2001. p. 268-82.
25. Kristensen K.J., and Jensen S.E. A model for estimating actual evapotranspiration from potential evapotranspiration. Nordic Hydrol 6 (1975) 70-88
26. Kohonen T. Self-organized formation of topologically correct feature maps. Biol Cybern 43 (1982) 59-69
27. Kuczera G., and Mroczkowski M. Assessment of hydrologic parameter uncertainty and the worth of multi-response data. Water Resour Res 35 (1998) 1481-1489
28. Di Luzio M., and Arnold J.G. Formulation of a hybrid calibration approach for a physically based distributed model with NEXRAD data input. J Hydrol 298 (2004) 136-154
29. Madsen H. Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. J Hydrol 235 (2000) 276-288
30. Madsen H. Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv Water Resour 26 (2003) 205-216
31. Madsen H, Khu ST. On the use of Pareto optimization for multi-criteria calibration of hydrological models, calibration and reliability in groundwater modelling: from uncertainty to decision making. In: Bierkens MFP, Gehrels JC, Kovar K, editors. Proceedings of ModelCARE 2005, The Hague, The Netherlands, June 2005. IAHS Publication No. 304, 2006. p. 93-9.
32. McCabe M.F., Franks S.W., and Kalma J.D. Calibration of a land surface model using multiple data sets. J Hydrol 302 (2005) 209-222
33. McLaughlin D., and Townley L.R. A reassessment of the groundwater inverse problem. Water Resour Res 32 5 (1996) 1131-1161
34. Meixner T., Bastidas L.A., Gupta H., and Bales R.C. Multicriteria parameter estimation for models of stream chemical composition. Water Resour Res 38 3 (2002) 1027 10.1029/2000WR000112
35. Mroczkowski M., Raper G.P., and Kuczera G. The quest for more powerful validation of conceptual catchment models. Water Resour Res 33 10 (1997) 2325-2335
36. Nash J.E., and Sutcliffe J. River flow forecasting through conceptual models, Part I: a discussions of principles. J Hydrol 10 (1970) 282-290
37. di Pierro F, Khu S-T, Savic DA. An investigation on preference ordering ranking scheme in multiobjective evolutionary optimization, IEEE Trans Evol Comput 2007;11(1):17-45. doi:10.1109/TEVC.2006.876362.
38. di Pierro F. Evolutionary many-objectives optimisation and applications to water resources engineering. PhD dissertation. University of Exeter, 2006.
39. Purshouse RC, Fleming PJ. Evolutionary multi-objective optimisation: an exploratory analysis. In: Proceedings of the 2003 congress on evolutionary computation (CEC'2003), vol. 3. Canberra, Australia: IEEE Press; 2003. p. 2066-73.
40. Refsgaard J.C. Parameterisation, calibration and validation of distributed hydrological models. J Hydrol 198 (1997) 69-97
41. Savic DA. Single-objective vs. multiobjective optimisation for integrated decision support. Integrated assessment and decision support. In: Rizzoli AE, Jakeman AJ, editors. Proceedings of the 1st Biennial Mtg. of int. env. modelling and software society, vol. 1. Switzerland: Lugano; 2001. p. 7-12.
42. Savic D.A., and Khu S.-T. Applications of evolutionary computing in hydrological sciences. In: Anderson M.G. (Ed). Encyclopaedia of hydrological sciences (2005), John Wiley publishers 177-178
43. Schoups G., Hopmans J.W., Young C.A., Vrugt J.A., and Wallender W.W. Multi-criteria optimization of a regional spatially-distributed subsurface water flow model. J Hydrol 311 (2005) 20-48
44. Seibert J., Uhlenbrook S., Leibundgut C., and Haldin S. Multiscale calibration and validation of a conceptual rainfall-runoff model. Phys Chem Earth, Part B: Hydrol Oceans Atmos 25 1 (2000) 59-64
45. Smith M.B., Seo D.-J., Koren V.I., Reed S.M., Zhang Z., Duan Q., et al. The distributed model intercomparison project (DMIP): motivation and experiment design. J Hydrol 298 (2004) 4-26
46. Tang Y., Reed P., and Wagener T. How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?. Hydrol Earth Syst Sci 10 (2006) 289-307
47. Vrugt J.A., Gupta H.V., Bastidas L.A., Bouten W., and Sorooshian S. Effective and efficient algorithm for multi-objective optimization of hydrologic models. Water Resour Res 39 8 (2003) 1214 10.1029/2002WR001746
48. Watanabe S, Sakakibara K. The effectiveness of multiobjective optimizer in single-objective optimization environment. GECCO conference, June 25-29, Washington, DC, USA. ACM 1-59593-010-8/05/0006, 2005.
49. Wealands S.R., Grayson R.B., and Walker J.P. Quantitative comparison of spatial fields for hydrological model assessment - some promising approaches. Adv Water Resour 28 (2005) 15-32
50. Yapo P.O., Gupta H.V., and Sorooshian S. Multi-objective global optimization for hydrologic models. J Hydrol 204 1-4 (1998) 83-97