Abstract
Accurate forecasting of rainfall has been one of the most important issues in hydrological research. In this paper, a novel neural network technique, support vector regression (SVR), to monthly rainfall forecasting. The aim of this study is to examine the feasibility of SVR in monthly rainfall forecasting by comparing it with back–propagation neural networks (BPNN) and the autoregressive integrated moving average (ARIMA) model. This study proposes a novel approach, known as particle swarm optimization (PSO) algorithms, which searches for SVR’s optimal parameters, and then adopts the optimal parameters to construct the SVR models. The monthly rainfall in Guangxi of China during 1985–2001 were employed as the data set. The experimental results demonstrate that SVR outperforms the BPNN and ARIMA models based on the normalized mean square error (NMSE) and mean absolute percentage error (MAPE).
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References
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)
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)
Burlando, P., Rosso, R., Cadavid, L.G., Salas, J.D.: Forecasting of short-term rainfall using ARMA models. Journal of Hydrology 144, 193–221 (1993)
Valverde, M.C., Campos Velho, H.F., Ferreira, N.J.: Artificial neural network technique for rainfall forecasting applied to The Sö Paulo Region. Journal of Hydrology 301(1-4), 146–162 (2005)
Luk, K.G., Ball, J.E., Sharma, A.: An application of artificial neural network for rainfall forecasting. Mathematical and Computer Modeling 33, 683–693 (2001)
Lin, G.F., Chen, L.H.: Application of an artificial neural network to typhoon rainfall forecasting. Hydrological Processes 19, 1825–1837 (2005)
Luk, K.G., Ball, J.E., Sharma, A.: Study of optimal lag and statistical inputs to artificial neural network for rainfall forecasting. Journal of Hydrology 227, 56–65 (2000)
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)
Vapnik, V.N.: Statistical learning theory. Wiley, New Yourk (1998)
Tay, F.E.H., Cao, L.: Modified support vector machines in financial time series forecasting. Neurocomputing 48(1-4), 847–861 (2002)
Vapnik, V., Golowich, S., Smola, A.: Support vector method for function approximation, regression estimation and signal processing. In: Mozer, M., Jordan, M., Petsche, T. (eds.) Advance in Neural Information Processing System, pp. 281–287. MIT, Cambridge (1997)
Schökopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, pp. 465–479. MIT Press, Cambridge (2002)
Hastie, T., Tibshirani, R., Friedman, J.H.: The elements of statistical learning: data mining, in- ference, and prediction, pp. 314–318. Springer, Heidelberg (2001)
Keerthi, S.S.: Efficient tuning of SVM hyper–parameters using radius/margin bound and iterative algorithms. IEEE Tranaction of the Neural Network 13(5), 1225–1229 (2000)
Duan, K., Keerthi, S., Poo, A.: Evaluation of simple performance measures for tuning SVM hyperparameters. Technical report, National University of Singapore, Singapore (2001)
Lin, P.T.: Support vector regression: systematic design and performance analysis. Doctoral Dissertation, Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei (2001)
Schölkopf, B., Smola, A., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Computation 5, 1207–1245 (2000)
Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2002)
Lin, S.W., Ying, K.C., Chen, S.C., Lee, Z.J.: Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications 35, 1817–1824 (2008)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Press, San Francisco (2001)
Smeral, E., Witt, S.F., Witt, C.A.: Econometric forecasts: Tourism trends to 2000. Annals of Tourism Research 19(3), 450–466 (1992)
Box, G.E.P., Jenkins, G.M.: Time series analysis: Forecasting and control. Holden-Day, San Francisco (1976)
Zhang, G., Hu, Y.: Neural network forecasting of the British Pound/US Dollar exchange rate. Omega 26(4), 495–506 (1998)
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Zhao, S., Wang, L. (2010). The Model of Rainfall Forecasting by Support Vector Regression Based on Particle Swarm Optimization Algorithms. In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15597-0_13
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DOI: https://doi.org/10.1007/978-3-642-15597-0_13
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