Abstract
The practical experience with sensitivity analysis suggests that no single-objective function is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. In order to successfully measure parameter sensitivity of a numerical model, multiple criteria should be considered. Sensitivity analysis of a rainfall-runoff model is performed using the local sensitivity method (Morris method) and multiple objective analysis. Formulation of SA strategy for the MIKE/NAM rainfall-runoff model is outline. The SA is given as a set of Pareto ranks from a multi-objective viewpoint. The Nondominated Sorting Differential Evolution (NSDE) was used to calibrate the rainfall-runoff model. The method has been applied for calibration of a test catchment and compared on validation data. The simulations show that the NSDE method possesses the ability to finding the optimal Pareto front.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S.: Global Sensitivity Analysis. The Primer. John Wiley & Sons, Chichester (2008)
Yue, H., Brown, M., Knowles, J., Wang, H., Broomhead, D.S., Kell, D.B.: Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-kB signaling pathway. Molecular Biosystems 2(12), 640–649 (2006)
Yapo, P.O., Gupta, H.V., Sorooshian, S.: Multi-objective global optimisation for hydrologic models. Journal of Hydrology 204, 83–97 (1998)
Madsen, H.: Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. Journal of Hydrology 235, 276–288 (2000)
Cooper, V.A., Nguyen, V.T.V., Nicell, J.A.: Evaluation of global optimisation methods for conceptual rainfall-runoff model calibration. Water Science and Technology 36(5), 53–60 (1997)
Liu, Y., Khu, S.T., Savic, D.A.: A fast hybrid optimisation method of multi-objective genetic algorithm and k-nearest neighbour classifier for hydrological model calibration. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 546–551. Springer, Heidelberg (2004)
Sorooshian, S., Gupta, H.V.: Calibration of hydrological models using multi-objectives and visualization techniques. Final Report (EAR-9418147), Department of hydrology and water resources, the University of Arizona, Tucson, AZ (1998)
Janssen, P.H.M., Heuberger, P.S.C.: Calibration of process-oriented models. Ecological Modelling 83, 55–66 (1995)
Liu, Y.: Automatic Calibration of a Rainfall-Runoff Model Using a Fast and Elitist Multi-objective Particle Swarm Algorithm. Expert Systems with Applications 36(5), 9533–9538 (2009)
Morris, M.D.: Factorial Sampling Plans for Preliminary Computational Experiments. Technometrics 33, 161–174 (1991)
Storn, R., Price, K.: Differential evolution: a simple and efficient adaptive scheme for global optimisation over continuous spaces. Technical report tr-95-012, international computer science institute, Berkley (1995)
Goldberg, D.E.: Genetic Algorithms in Search, Optimisation, and Machine Learning. Addison-Wesley Publishing Co., Reading (1989)
Iorio, A., Li, X.: Solving Rotated Multi-objective Optimization Problems Using Differential Evolution. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 861–872. Springer, Heidelberg (2004)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Knowles, J., Corne, D.: On metrics for comparing non-dominated Sets. In: Congress on Evolutionary Computation, pp. 711–716 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, F., Liu, Y. (2010). Sensitivity Analysis and Automatic Calibration of a Rainfall-Runoff Model Using Multi-objectives. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15387-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-15387-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15386-0
Online ISBN: 978-3-642-15387-7
eBook Packages: Computer ScienceComputer Science (R0)