Abstract
In this paper, a new approach using an Modular Radial Basis Function Neural Network (M-RBF-NN) technique is presented to improve rainfall forecasting performance coupled with appropriate data–preprocessing techniques by Singular Spectrum Analysis (SSA) and Partial Least Square (PLS) regression. In the process of modular modeling, SSA is applied for the time series extraction of complex trends and finding structure. In the second stage, the data set is divided into different training sets by used Bagging and Boosting technology. In the third stage, then modular RBF–NN predictors are produced by different kernel function. In the fourth stage, PLS technology is used to choose the appropriate number of neural network ensemble members. In the final stage, least squares support vector regression is used for ensemble of the M-RBF-NN to prediction purpose. The developed RBF-NN model is being applied for real time rainfall forecasting and flood management in Liuzhou, Guangxi. Aimed at providing forecasts in a near real time schedule, different network types were tested with the same input information. Additionally, forecasts by M-RBF-NN model were compared to the convenient approach. Results show that that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Sensitivity analysis indicated that the proposed M-RBF-NN technique provides a promising alternative to rainfall prediction.
Keywords
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
Wu, J., Liu, M.Z., Jin, L.: A Hybrid Support Vector Regression Approach for Rainfall Forecasting Using Particle Swarm Optimization and Projection Pursuit Technology. International Journal of Computational Intelligence and Applications 9(3), 87–104 (2010)
Wu, J., Jin, L.: Study on the Meteorological Prediction Model Using the Learning Algorithm of Neural Networks Ensemble Based on PSO agorithm. Journal of Tropical Meteorology 15(1), 83–88 (2009)
French, M.N., Krajewski, W.F., Cuykendall, R.R.: Rainfall Forecasting in Space and Time Using Neural Network. Journal of Hydrology 137, 1–31 (1992)
Gwangseob, K., Ana, P.B.: Quantitative Flood Forecasting Using Multisensor Data and Neural Networks. Journal of Hydrology 246, 45–62 (2001)
Parag, P., Preeti, B., Ajith, A., Prasanna, P., Amol, D.: Human Perception-based Color Image Segmentation Using Comprehensive Learning Particle Swarm Optimization. Journal of Information Hiding and Multimedia Signal Processing 2(3), 227–235 (2011)
Partalas, I., Hatzikos, E., Tsoumakas, G., Vlahavas, I.: Ensemble Selection for Water Quality Prediction. In: Proeedings of 10th International Conference on Engineering Applications of Neural Networks, pp. 428–435 (2007)
Broomhead, D.S., King, G.P.: Extracting Qualitative Dynamics from Experimental Data. Physica D 20, 217–236 (1986)
Alexandrov, T., Bianconcini, S., Dagum, E.B., Maass, P., McElroy, T.S.: A Review of Some Modern Approaches to The Problem of Trend Extraction. Technical report, US Census Bureau RRS2008/03 (2008)
Wu, J.: A Semiparametric Regression Ensemble Model for Rainfall Forecasting Based on RBF Neural Network. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds.) AICI 2010, Part II. LNCS (LNAI), vol. 6320, pp. 284–292. Springer, Heidelberg (2010)
Moravej, Z., Vishwakarma, D.N., Singh, S.P.: Application of Radial Basis Function Neural Network for Differential Relaying of a Power Transformer. Computers and Electrical Engineering 29, 421–434 (2003)
Ham, F.M., Kostanic, I.: Principles of Neurocomputing for Science & Engineering. The McGraw-Hill Companies, New York (2001)
Wold, S., Ruhe, A., Wold, H., Dunn, W.J.: The Collinearity Problem in Linear Regression: the Partial Least Squares Approach to Generalized Inverses. Journal on Scientific and Statistical Computing 5(3), 735–743 (1984)
Pirouz, D.M.: An Overview of Partial Least Square. Technical report, The Paul Merage School of Business, University of California, Irvine (2006)
Suykens, J., Gestel, T., Van, J.: Least Squares Support Vector Machines. The World Scientific Publishing, Singapore (2002)
Schökopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press, Cambridge (2002)
Wang, H., Li, E., Li, G.Y.: The Least Square Support Vector Regression Coupled with Parallel Sampling Scheme Metamodeling Technique and Application in Sheet Forming Optimization. Materials and Design 30, 1468–1479 (2009)
Chang, F.C., Huang, H.C.: A Refactoring Method for Cache-Efficient Swarm Intelligence Algorithms. Information Sciences, doi:10.1016/j.ins.2010.02.025
Wu, J.: An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression. International Journal of Applied Evolutionary Computation 2(4), 50–65 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, J. (2012). Prediction of Rainfall Time Series Using Modular RBF Neural Network Model Coupled with SSA and PLS. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28490-8_53
Download citation
DOI: https://doi.org/10.1007/978-3-642-28490-8_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28489-2
Online ISBN: 978-3-642-28490-8
eBook Packages: Computer ScienceComputer Science (R0)