Multi-objective calibration and fuzzy preference selection of a distributed hydrological model

Environmental Modelling and Software

Volumn 23, Issue 12, 2008, Pages 1384-1395

  Raj Shrestha, Rajesh ^a   Rode, Michael ^b  

^a UNIVERSITY OF WESTERN ONTARIO   (Canada)

^b HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH UFZ   (Germany)

Author keywords

Distributed hydrological model; Fuzzy set; Genetic algorithm; Multi objective calibration; Preference selection; Streamflow uncertainty; Water balance

Indexed keywords

CALIBRATION; CATCHMENTS; MULTIOBJECTIVE OPTIMIZATION; SOLUTE TRANSPORT;

DISTRIBUTED HYDROLOGICAL MODEL; GAUGING STATIONS; HYDROLOGICAL MODELLING; INDIVIDUAL (PSS 544-7); MULTI OBJECTIVES; MULTI-RESPONSE; SUB-SYSTEMS;

HYDRAULIC MODELS;

CALIBRATION; CATCHMENT; FUZZY MATHEMATICS; GAUGE; GENETIC ALGORITHM; HYDROLOGICAL MODELING; OPTIMIZATION; PREDICTION; RIVER CHANNEL; STREAMFLOW; WATER BUDGET;

CENTRAL EUROPE; ELBE BASIN; EURASIA; EUROPE; GERMANY;

EID: 47849123305     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2008.04.001     Document Type: Article

References (45)

1. Ajami N.K., Gupta H., Wagener T., and Sorooshian S. Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology 298 (2004) 112-135
2. Andersen J., Refsgaard J.C., and Jensen H.J. Distributed hydrological modeling of the Senegal River basin-model construction and validation. Journal of Hydrology 247 (2001) 200-214
3. Anderton S., Latron J., White S.M., and Gallart F. Sensitivity analysis and multi-response, multi-criteria evaluation of a physically based distributed model. Hydrological Processes 16 (2002) 333-353
4. Bárdossy A., and Duckstein L. Fuzzy Rule Based Modelling with Application to Geophysical, Biological and Engineering Systems (1995), CRC Press Inc., Boca Raton, Florida, USA
5. Bergström S., Lindström G., and Pettersson A. Multi-variable parameter estimation to increase confidence in hydrological modelling. Hydrological Processes 16 (2002) 413-421
6. Beven K., and Freer J. Equifinality, data assimilation, and data uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology 249 (2001) 11-29
7. Beven K.J., and Kirkby M.J. A physically based variable contributing area model of basin hydrology. Hydrological Sciences Bulletin 24 1 (1979) 43-69
8. Boughton W. Calibrations of a daily rainfall-runoff model with poor quality data. Environmental Modelling & Software 21 8 (2006) 1114-1128
9. Cheng C.T., Ou C.P., and Chau K.W. Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration. Journal of Hydrology 268 (2002) 72-86
10. Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6 2 (2002) 188-197
11. Deb K. Evolutionary Multi-objective Optimisation Without Additional Parameters. KanGAL Report Number 2006003 (2006), Indian Institute of Technology, Kanpur, India
12. Duan Q., Gupta V.K., and Sorooshian S. Effective and efficient global optimisation for conceptual rainfall-runoff models. Water Resources Research 28 4 (1992) 1015-1031
13. Engeland K., Braud I., Gottschalk L., and Leblois E. Multi-objective regional modelling. Journal of Hydrology 327 (2006) 339-351
14. Fernald A.G., Wigington P.J., and Landers D.H. Transient storage and hyporheic flow along the Willamette River, Oregon: field measurements and model estimates. Water Resources Research 37 (2001) 1681-1694
15. Fu G.T., Butler D., and Khu S.T. Multiple objective optimal control of integrated urban wastewater systems. Environmental Modelling & Software 23 (2008) 225-234
16. Goldberg D.E. Genetic Algorithms in Search, Optimisation and Machine Learning (1989), Addison-Wesley, Boston, Massachusetts
17. Grayson R.B., Blöschl G., and Moore I.D. Distributed parameter hydrologic modelling using vector elevation data: THALES and TAPES-C. In: Singh V.P. (Ed). Computer Models in Watershed Hydrology (1995), Water Resources Publications 669-696
18. 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
19. Hinkle S.R., Duff J.H., Triska F.J., Laenen A., Gates E.B., Bencala K.E., Wentz D.A., and Silva S.R. Linking hyporheic flow and nitrogen cycling near the Willamette River - a large river in Oregon, USA. Journal of Hydrology 244 3-4 (2001) 157-180
20. Kim T., Heo J.-H., and Jeong C.-S. Multireservoir system optimisation in the Han River basin using multi-objective genetic algorithms. Hydrological Processes 20 (2006) 2057-2075
21. Khu S.T., and Madsen H. Multiobjective calibration with Pareto preference ordering: an application to rainfall-runoff model calibration. Water Resources Research 41 (2005) W03004
22. Khu S.T., di Pierro F., Savic D., Djordjevic S., and Walters G.A. Incorporating spatial and temporal information for urban drainage model calibration: an approach using Pareto preference ordering genetic algorithm. Advances in Water Resources 29 (2006) 1168-1181
23. Kollat J.B., and Reed P. A framework for visually interactive decision-making and design using evolutionary multi-objective optimization (VIDEO). Environmental Modelling & Software 22 12 (2007) 1691-1704
24. Konrad C.P. Location and timing of river-aquifer exchanges in six tributaries to the Columbia River in the Pacific Northwest of the United States. Journal of Hydrology 329 3-4 (2006) 444-470
25. Legates D.R., and McCabe G.J. Evaluation of the use of "goodness of fit" measures in hydrological and hydroclimatic model validation. Water Resources Research 35 1 (1999) 233-241
26. LfUG. Personal Correspondence (2007), Saxony State Office for Environment and Geology (LfUG)
27. Lindenschmidt K.E., Ollesch G., and Rode M. Physically-based hydrological modelling for nonpoint dissolved phosphorus transport in small and medium-sized river basins. Hydrological Sciences Journal 49 3 (2004) 495-510
28. Madsen H. Parameter estimation in distributed hydrological catchment modeling using automatic calibration with multiple objectives. Advances in Water Resources 26 (2003) 205-216
29. Reed P., Kollat J.B., and Devireddy V.K. Using interactive archives in evolutionary multiobjective optimization: A case study for long-term groundwater monitoring design. Environmental Modelling & Software 22 5 (2007) 683-692
30. Reed P., Minsker B., and Goldberg D.E. A multiobjective approach to cost effective long-term groundwater monitoring using an Elitist nondominated sorted genetic algorithm with historical data. Journal of Hydroinformatics 3 2 (2001) 71-90
31. Refsgaard J.C. Parameterisation, calibration and validation of distributed hydrological models. Journal of Hydrology 198 (1997) 69-97
32. Rode M., and Lindenschmidt K.E. Distributed sediment and phosphorus transport modeling on a medium sized catchment in Central Germany. Physics and Chemistry of the Earth (B) 26 7-8 (2001) 635-640
33. Schulla, J., 1997. Hydrologische Modellierung von Flussgebieten zur Abschätzung der Folgen von Klimaänderungen. Dissertation. ETH-Zurich, Switzerland (in German).
34. Schulla J., and Jasper K. Model Description WaSiM-ETH. Internal Report (2001), ETH-Zurich, Switzerland
35. Seshadri A. A Fast Elitist Multiobjective Genetic Algorithm: NSGA-II (2006), Matlab Implementation. http://www.mathworks.com/matlabcentral
36. Siebert J., Uhlenbrook S., Leibundgut C., and Halldin S. Multiscale calibration and validation of a conceptual rainfall-runoff model. Physics and Chemistry of the Earth (B) 25 1 (2000) 59-64
37. Shrestha R.R., Bárdossy A., and Nestmann F. Analysis and propagation of uncertainties due to stage discharge relationship: a fuzzy set approach. Hydrological Sciences Journal 52 4 (2007) 595-610
38. Shrestha R.R., Bárdossy A., and Rode M. A hybrid deterministic - fuzzy rule based model for catchment scale nitrate dynamics. Journal of Hydrology 342 (2007) 143-156
39. Van Veldhuizen D.A., and Lamont G.B. Multiobjective evolutionary algorithms: analyzing the state-of-the-art. Evolutionary Computation 8 2 (2000) 125-147
40. Vrugt J.A., Gupta H.V., Bastidas L.A., Bouten W., and Sorooshian S. Effective and efficient algorithm for multiobjective optimisation of hydrologic models. Water Resources Research 39 8 (2003) 1214
41. Yang T.-C., and Yu P.-S. Application of fuzzy multi-objective function on reducing groundwater demand for aquaculture in land-subsidence areas. Water Resources Management 20 (2006) 377-390
42. Yapo P.O., Gupta H.V., and Sorooshian S. Multi-objective global optimisation for hydrologic models. Journal of Hydrology 204 (1998) 83-97
43. Yu P.-S., and Yang T.-C. Fuzzy multi-objective function for rainfall-runoff model calibration. Journal of Hydrology 238 (2000) 1-14
44. Zitzler E., and Thiele L. Multiobjective optimisation using evolutionary algorithms - a comparative study. In: Eiben A.E. (Ed). Parallel Problem Solving from Nature (V) (1998), Springer-Verlag, Amsterdam 292-301
45. Zitzler E., Laumanns M., and Thiele L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. TIK-103 (2001), Department of Electrical Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland