×

New optimal weight combination model for forecasting precipitation. (English) Zbl 1264.86017

Summary: In order to overcome the inaccuracy of the forecast of a single model, a new optimal weight combination model is established to increase accuracies in precipitation forecasting, in which three forecast submodels based on rank set pair analysis (R-SPA) model, radical basis function (RBF) model and autoregressive model (AR) and one weight optimization model based on improved real-code genetic algorithm (IRGA) are introduced. The new model for forecasting precipitation time series is tested using the annual precipitation data of Beijing, China, from 1978 to 2008. Results indicate the optimal weights were obtained by using genetic algorithm in the new optimal weight combination model. Compared with the results of R-SPA, RBF, and AR models, the new model can improve the forecast accuracy of precipitation in terms of the error sum of squares. The amount of improved precision is 22.6%, 47.4%, 40.6%, respectively. This new forecast method is an extension to the combination prediction method.

MSC:

86A32 Geostatistics
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] X. H. Yang, D. X. She, Z. F. Yang, Q. H. Tang, and J. Q. Li, “Chaotic bayesian method based on multiple criteria decision making (MCDM) for forecasting nonlinear hydrological time series,” International Journal of Nonlinear Sciences and Numerical Simulation, vol. 10, no. 11-12, pp. 1595-1610, 2009. · Zbl 06942535
[2] M. Li, C. Cattani, and S. Y. Chen, “Viewing sea level by a one-dimensional random function with long memory,” Mathematical Problems in Engineering, vol. 2011, Article ID 654284, 13 pages, 2011. · Zbl 05829261 · doi:10.1155/2011/654284
[3] K. W. Chau, “Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River,” Journal of Hydrology, vol. 329, no. 3-4, pp. 363-367, 2006. · doi:10.1016/j.jhydrol.2006.02.025
[4] X. H. Yang, X. J. Zhang, X. X. Hu, Z. F. Yang, and J. Q. Li, “Nonlinear optimization set pair analysis model (NOSPAM) for assessing water resource renewability,” Nonlinear Processes in Geophysics, vol. 18, no. 5, pp. 599-607, 2011.
[5] K. W. Chau, C. L. Wu, and Y. S. Li, “Comparison of several flood forecasting models in Yangtze River,” Journal of Hydrologic Engineering, vol. 10, no. 6, pp. 485-491, 2005. · doi:10.1061/(ASCE)1084-0699(2005)10:6(485)
[6] J. X. Xu, Y. D. Liu, Z. Z. Zhang, and Y. B. Li, “Ordinal-set pair prediction model and application in Liao river basin,” in Computer Informatics Cybernetics and Applications, vol. 1 of Lecture Notes in Electrical Engineering 107, chapter 42, pp. 395-402, 2012.
[7] D. Broomhead and D. Lowe, “Multivariable functional interpolation and adaptive networks,” Complex Systems, vol. 2, pp. 321-355, 1988. · Zbl 0657.68085
[8] M. Alp and H. K. Cigizoglu, “Suspended sediment load simulation by two artificial neural network methods using hydro meteorological data,” Environmental Modelling & Software, vol. 22, no. 1, pp. 2-13, 2007.
[9] Q. Duan, N. K. Ajami, X. Gao, and S. Sorooshian, “Multi-model ensemble hydrologic prediction using Bayesian model averaging,” Advances in Water Resources, vol. 30, no. 5, pp. 1371-1386, 2007. · doi:10.1016/j.advwatres.2006.11.014
[10] J. M. Bates and C. W. J. Granger, “The combination of forecasts,” Operational Research Quarterly, vol. 20, no. 4, pp. 451-468, 1969.
[11] J. P. Dickinson, “Some statistical results in the combination of forecasts,” Operational Research Quarterly, vol. 24, no. 2, pp. 253-260, 1973.
[12] J. P. Dickinson, “Some comments on the combination of forecasts,” Operational Research Quarterly, vol. 26, no. 1, pp. 205-210, 1975. · Zbl 0312.62068 · doi:10.1057/jors.1975.43
[13] P. Newbold and C. W. J. Granger, “Experience with forecasting univariate time series and the combination of forecasts,” Journal of the Royal Statistical Society A, vol. 137, no. 2, pp. 131-146, 1974.
[14] M. Li and W. Zhao, “Visiting power laws in cyber-physical networking systems,” Mathematical Problems in Engineering, vol. 2012, Article ID 302786, 13 pages, 2012. · Zbl 06173174 · doi:10.1155/2012/302786
[15] A. Y. Shamseldin and K. M. O’Connor, “A real-time combination method for the outputs of different rainfall-runoff models,” Hydrological Sciences Journal, vol. 44, no. 6, pp. 895-912, 1999.
[16] A. E. Raftery, F. Balabdaoui, T. Gneiting, and M. Polakowski, “Using Bayesian model averaging to calibrate forecast ensembles,” Monthly Weather Review, vol. 133, no. 5, pp. 1155-1174, 2005. · doi:10.1175/MWR2906.1
[17] C. T. Cheng, C. P. Ou, and K. W. Chau, “Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration,” Journal of Hydrology, vol. 268, no. 1-4, pp. 72-86, 2002. · doi:10.1016/S0022-1694(02)00122-1
[18] N. K. Ajami, Q. Duan, X. Gao, and S. Sorooshian, “Multi-model combination techniques for hydrological forecasting: application to distributed model intercomparison project results,” Journal of Hydrometeorology, vol. 7, no. 8, pp. 755-768, 2006.
[19] X. H. Yang, Y. N. Guo, Y. Q. Li, and L. H. Geng, “Projection pursuit hierarchy model based on chaos real-code genetic algorithm for river health assessment,” Nonlinear Science Letters C, vol. 1, no. 1, pp. 1-13, 2011.
[20] A. Y. Shamseldin, K. M. O’Connor, and G. C. Liang, “Methods for combining the outputs of different rainfall-runoff models,” Journal of Hydrology, vol. 197, no. 1-4, pp. 203-229, 1997. · doi:10.1016/S0022-1694(96)03259-3
[21] N. Muttil and K. W. Chau, “Neural network and genetic programming for modelling coastal algal blooms,” International Journal of Environment and Pollution, vol. 28, no. 3-4, pp. 223-238, 2006. · doi:10.1504/IJEP.2006.011208
[22] A. M. Taurino, C. Distante, P. Siciliano, and L. Vasanelli, “Quantitative and qualitative analysis of VOCs mixtures by means of a microsensors array and different evaluation methods,” Sensors and Actuators B, vol. 93, no. 1-3, pp. 117-125, 2003. · doi:10.1016/S0925-4005(03)00241-7
[23] I. Vilibic and N. Leder, “Long-term variations in the Mediterranean sea level calculated by spectral analysis,” Oceanologica Acta, vol. 19, no. 6, pp. 599-607, 1996.
[24] D. H. Fitzpatrick and S. B. Caroline, “Spectral analysis of pressure variations during combined air and water backwash of rapid gravity filters,” Water Research, vol. 33, no. 17, pp. 3666-3672, 1999. · doi:10.1016/S0043-1354(99)00092-5
[25] P. J. Brockwell and A. D. Richard, Time Series Theory and Methods, Springer; Chep, Berlin, Germany, 2001.
[26] G. M. Morris, D. S. Goodsell, R. S. Halliday et al., “Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function,” Journal of Computational Chemistry, vol. 19, no. 14, pp. 1639-1662, 1998.
[27] K. Deb, “An efficient constraint handling method for genetic algorithms,” Computer Methods in Applied Mechanics and Engineering, vol. 186, no. 2-4, pp. 311-338, 2000. · Zbl 1028.90533 · doi:10.1016/S0045-7825(99)00389-8
[28] X. B. Hu, M. S. Leeson, and E. L. Hines, “An effective genetic algorithm for network coding,” Computers and Operations Research, vol. 39, no. 5, pp. 952-963, 2012. · Zbl 1251.68035 · doi:10.1016/j.cor.2011.07.014
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.