Abstract
Accurate forecasting of rainfall has been one of the most important issues in hydrological research. In this paper, a novel nonlinear regression ensemble model is proposed for rainfall forecasting. The model employs Least Square Support Vector Machine (LS-SVM) based on linear regression and nonlinear regression. Firstly, Projection Pursuit (PP) technology and Particle Swarm Optimization (PSO) algorithm are used to obtain the main factors of the rainfall, which optimize projection index from high dimensionality to a lower dimensional subspace. Secondly, using different linear regressions extract linear characteristics of the rainfall system, and using different Neural Network (NN) algorithms and different network architectures extract nonlinear characteristics of the rainfall system. Finally, LS-SVM regression is used for nonlinear ensemble model. This technique is implemented to forecast daily rainfall in Guangxi, China. Empirical results show that the prediction by using the LS-SVM ensemble model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. The results suggest that our nonlinear ensemble model can be extended to meteorological applications in achieving greater forecasting accuracy and improving prediction quality.
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References
Jiangsheng, W., Long, J.: Forecast Research and Applying of BP Neural Network Based on Genetic Algorithms. Mathematics in Practice and Theory 35(1), 83–88 (2005)
Jiansheng, W., Long, J., Mingzhe, L.: Modeling Meteorological Prediction Using Particle Swarm Optimization and Neural Network Ensemble. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 1202–1209. Springer, Heidelberg (2006)
Nasseri, M., Asghari, K., Abedini, M.J.: Optimized Scenario for Rainfall Forecasting Using Genetic Algorithm Coupled with Artificial Neural Network. Expert Systems with Application 35, 1414–1421 (2008)
Jiansheng, W., Enhong, C.: A Novel Nonparametric Regression Ensemble for Rainfall Forecasting Using Particle Swarm Optimization Technique Coupled with Artificial Neural Network. In: Yu, W., He, H., Zhang, N. (eds.) ISNN 2009. LNCS, vol. 5553, pp. 49–58. Springer, Heidelberg (2009)
Yingni, J.: Prediction of Monthly Mean Daily Diffuse Solar Radiation Using Artificial Neural Networks and Comparison with other Empirical Models. Energy Policy 36, 3833–3837 (2008)
Govindaraju, R.S.: Artificial Neural Network in Hydrology, I: Preliminary Concepts. Journal of Hydrologic Engineering 5(2), 115–123 (2000)
French, M.N., Krajewski, W.F., Cuykendal, R.R.: Rainfall Forecasting in Space and Time Using a Neural Network. Journal of Hydrology 137, 1–37 (1992)
Friedman., J.H., Turkey, J.W.: A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transaction On Computers 3(9), 881–889 (1974)
Filzmoser, P., Serneels, S., Croux, C., Van Espen, P.J.: Robust Multivariate Methods: The Projection Pursuit Approach. In: Spiliopoulou, M., Kruse, R., Nürnberger, A., Borgelt, C., Gaul, W. (eds.) From Data and Information Analysis to Knowledge Engineering Proceedings of the 29th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Magdeburg, vol. 8, pp. 270–277. Springer, Heidelberg (2006)
Johenson, D.: Applied Ultivariate Methods for Data Analysts, 2nd edn. Thomson Learning Press, UK (1998)
Helland, I.S.: PLS Regression and Statistical Models. Scandivian Journal of Statistics 179, 97–114 (1990)
Kenneth, D.S.: Using recursive regression to explore nonlinear relationships and interactions: A tutorial applied to a multicultural education study. Practical Assessment Research and Evaluation 14(3), 1–13 (2009) (Retrieved March 3, 2009) http://pareonline.net/getvn.asp
Jiansheng, W.: A Novel Artificial Neural Network Ensemble Model Based on K–nn Nonparametric Estimation of Regression Function and Its Application for Rainfall Forecasting. In: 2nd Internatioal Joint Conference on Computational Sciences and Optimization, pp. 44–48. IEEE Computer Society Press, New York (2009)
Benediktsson, J.A., Rveinsson, J., Ersoy, O.K., Swain, P.H.: Parallel Consensual Neural Neural Networks. IEEE Transactions on Neural Networks 8, 54–64 (1997)
Fredric, M.H., Ivica, K.: Principles of Neurocomputing for Science & Engineering. McGraw-Hill, New York (2001)
Suykens, J., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific Publishing Press, Singapore (2002)
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Wu, J., Liu, M., Jin, L. (2010). Least Square Support Vector Machine Ensemble for Daily Rainfall Forecasting Based on Linear and Nonlinear Regression. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_7
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DOI: https://doi.org/10.1007/978-3-642-12990-2_7
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