×

A hybrid model for the mid-long term runoff forecasting by evolutionary computation techniques. (English) Zbl 1062.68604

Summary: The mid-long term hydrology forecasting is one of most challenging problems in hydrological studies. This paper proposes an efficient dynamical system prediction model using evolutionary computation techniques. The new model overcomes some disadvantages of conventional hydrology forecasting ones. The observed data is divided into two parts: the slow “smooth and steady” data, and the fast “coarse and fluctuation” data. Under the divide and conquer strategy, the behavior of smooth data is modeled by ordinary differential equations based on evolutionary modeling, and that of the coarse data is modeled using gray correlative forecasting method. Our model is verified on the test data of the mid-long term hydrology forecast in the northeast region of China. The experimental results show that the model is superior to gray system prediction model.

MSC:

68U20 Simulation (MSC2010)
86A05 Hydrology, hydrography, oceanography
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Xia, J.; Hu, B. Q., A Grey System Model for Predicting Trend Change of Urban Waste Water Load, J Environmental Hydrology, 1, 1-10 (1997)
[2] Fang, K.; Quan, H., Applied Regress Analysis (1988), Beijing: Science Press, Beijing
[3] Sugeno, M.; Yasukawa, T., A Fuzzy Logic-based Approach to Qualitative Modeling, IEEE Transon Fuzzy Systems, 1, 1, 7-31 (1993) · doi:10.1109/TFUZZ.1993.390281
[4] Hu B Q.Research On Theory of Extension and Incidence Analysis and Their Applications to Hydrology and Water Environment: [Ph. D. thesis]. Wuhan University, 2001 (Ch).
[5] Iba, H.; Sasaki, T., Using Genetic Programming to Predict Financial Data, Proceedings of the Congress on Evolutionary Computation, 1, 244-251 (1999)
[6] Michalewicz, Z., Genetic Algorithms+Data Structures=Evolution Programs (1996), Berlin, Germany: SpringerVerlag, Berlin, Germany · Zbl 0841.68047
[7] Schwefel, H. P., Evolution and Optimum Seeking (1995), Wuhan: Wiley, Wuhan
[8] Brameier, M.; Banzhaf, W., A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining, IEEE Trans on Evol Comp, 5, 1, 17-26 (2001) · doi:10.1109/4235.910462
[9] Koza, J. R., Genetic Programming II :Automatic Discovery of Reusable Programs (1994), Cambridge, MA: MIT Press, Cambridge, MA · Zbl 0850.68160
[10] Cao, H. Q.; Kang, L. S.; Chen, Y. P., Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming, Genetic Programming and Evolvable Machines, 1, 4, 309-337 (2000) · Zbl 1060.68569 · doi:10.1023/A:1010013106294
[11] Cao, H. Q.; Kang, L. S.; Guo, T., A Two-level Hybrid Evolutionary Algorithm For Modeling One-Dimensional Dynamic Systems by High-Order ODE, IEEE Trans on Systems, Man and Cybernetics, 30, 2, 351-357 (2000) · doi:10.1109/3477.836383
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.