Abstract
The MOGA is used as automatic calibration method for a wide range of water and environmental simulation models.The task of estimating the entire Pareto set requires a large number of fitness evaluations in a standard MOGA optimization process. However, it’s very time consuming to obtain a value of objective functions in many real engineering problems. We propose a unique hybrid method of MOGA and KNN classifier to reduce the number of actual fitness evaluations. The test results for multi-objective calibration show that the proposed method only requires about 30% of actual fitness evaluations of the MOGA.
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References
Madsen, H.: Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. Journal of Hydrology 235, 276–288 (2000)
Yapo, P.O., Gupta, H.V., Sorooshian, S.: Multi-objective global optimization for hydrologic models. Journal of Hydrology 181, 23–48 (1998)
Wang, Q.J.: The genetic algorithm and its application to calibrating conceptual rainfallrunoff models. Journal of Water Resource Research 27, 2467–2471 (1991)
Khu, S.T., Liu, Y., Madsen, H., Savic, D.A.: A fast evolutionary-based meta-modeling approach for the calibration of a rainfall-runoff model. In: The International Environmental Modeling and Software Society Conference (2004)
Khu, S.T., Liu, Y., Madsen, H., Savic, D.A.: A fast calibration technique using a hybrid Genetic algorithm – neural network approach: application to rainfall - runoff models. In: The Sixth International Conference of Hydroinformatics (2004)
Khu, S.T., Madsen, H.: A new approach to multi-criteria calibration of rainfall-runoff model. In: International Conference on Water and Environment: Watershed Hydrology (2003)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Dasarathy, B.V.: Nearest-Neighbor Classification Techniques. IEEE Computer Society Press, CA (1991)
Beale, R., Jackson, R.: Neural Computing: An Introduction. Institute of Physics Publishing, London (2001)
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© 2004 Springer-Verlag Berlin Heidelberg
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Liu, Y., Khu, ST., Savic, D. (2004). A Hybrid Optimization Method of Multi-objective Genetic Algorithm (MOGA) and K-Nearest Neighbor (KNN) Classifier for Hydrological Model Calibration. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_80
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DOI: https://doi.org/10.1007/978-3-540-28651-6_80
Publisher Name: Springer, Berlin, Heidelberg
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