Abstract
This paper aims to improve the prediction accuracy of Tropical Cyclone Tracks (TCTs) over the South China Sea (SCS) and its coastal regions with 24 h lead time. The model proposed in this paper is a regularized extreme learning machine (ELM) ensemble using bagging. A new method is proposed in this paper to solve lasso and elastic net problem in ELM, which turns the original problem into familiar quadratic programming (QP) problem. The forecast error of TCTs data set is the distance between real position and forecast position. Compared with the stepwise regression method widely used in TCTs, 16.49 km accuracy improvement is obtained by our model. Results show that the regularized ELM ensemble using bagging has a better generalization capactity on TCTs data set.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Roy, C., Kovordányi, R.: Tropical cyclone track forecasting techniques - a review. Atmos. Res. 104–105(1), 40–69 (2012)
Ali, M.M., Kishtawal, C.M., Jain, S.: Predicting cyclone tracks in the north Indian Ocean: an artificial neural network approach. Geophys. Res. Lett. 34(4), 545–559 (2007)
Wang, Y., Zhang, W., Fu, W.: Back Propogation(BP)-neural network for tropical cyclone track forecast, pp. 1–4 (2011)
Chaudhuri, S., Dutta, D., Goswami, S., Middey, A.: Track and intensity forecast of tropical cyclones over the North Indian Ocean with multilayer feed forward neural nets. Meteorol. Appl. 22(3), 563–575 (2015)
Zhu, L., Jin, J., Cannon, A.J., Hsieh, W.W.: Bayesian neural networks based bootstrap aggregating for tropical cyclone tracks prediction in South China sea. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9949, pp. 475–482. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46675-0_52
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. Proceedings of International Joint Conference on Neural Networks, vol. 2, pp. 985–990 (2004)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern. 2(2), 107–122 (2011)
Escandell-Montero, P., Soria-Olivas, E., Magdalena-Benedito, R.: Letters: regularized extreme learning machine for regression problems. Neurocomputing 74(17), 3716–3721 (2011)
Elad, M.: Sparse and Redundant Representations, pp. 3–14. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-7011-4
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B 42(2), 513 (2012)
Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1 (2010)
Boyd, S., Vandenberghe, L., Faybusovich, L.: Convex optimization. IEEE Trans. Autom. Control 51(11), 1859–1859 (2006)
Lee, T.C., Wong, M.S.: The use of multiple-model ensemble techniques for tropical cyclone track forecast at the Hong Kong Observatory. In: WMO Commission for Basic Systems Technical Conference on Data Processing and Forecasting Systems, pp. 554–565(12) (2002)
Wang Q: The study on ensemble prediction of typhoon track. J. Meteorol. Sci. (2012)
Goerss, J.S.: Tropical cyclone track forecasts using an ensemble of dynamical models. Mon. Weather Rev. 128(4), 1187 (2000)
Huang, X., Jin, L., Shi, X.: A nonlinear artificial intelligence ensemble prediction model based on EOF for typhoon track. In: International Joint Conference on Computational Sciences & Optimization, pp. 1329–1333. IEEE (2011)
Hansen, L.K.: Neural network ensemble. IEEE Trans. Pattern Anal. Mach. Intell. 12, 993–1001 (1990)
Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)
Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms. Taylor & Francis, Abingdon (2012)
Banerjee, K.S.: Generalized inverse of matrices and its applications. Technometrics 15(1), 197–197 (1971)
Bartlett, P.L.: The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans. Inf. Theory 44(2), 525–536 (1998)
Jin, L., Huang, X., Shi, X.: A study on influence of predictor multicollinearity on performance of the stepwise regression prediction equation. Acta Meteorologica Sinica 24, 593–601 (2010)
Sobol, I.M.: On the distribution of points in a cube and the approximate evaluation of integrals. USSR Comput. Math. Math. Phys. 7, 86–112 (1967)
Joe, S., Kuo, F.Y.: Remark on algorithm 659: implementing Sobol’s quasirandom sequence generator. ACM Trans. Math. Softw. (TOMS) 29, 49–57 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, J., Jin, J. (2018). Regularized Extreme Learning Machine Ensemble Using Bagging for Tropical Cyclone Tracks Prediction. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-02698-1_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02697-4
Online ISBN: 978-3-030-02698-1
eBook Packages: Computer ScienceComputer Science (R0)