Abstract
A new category called dynamic time series prediction is introduced to address robust “on the fly” prediction needed in events such as natural disasters. A co-evolutionary multi-task learning algorithm is presented which incorporates features from modular and multi-task learning. The algorithm is used for prediction of tropical cyclone wind-intensity. This addresses the need for a robust and dynamic prediction model during the occurrence of a cyclone. The results show that the method addresses dynamic time series effectively when compared to conventional methods.
This is a preview of subscription content, log in via an institution to check access.
References
Ali, M.M., Jagadeesh, P.S.V., Lin, I.I., Hsu, J.Y.: A neural network approach to estimate tropical cyclone heat potential in the Indian ocean. IEEE Geosci. Remote Sens. Lett. 9(6), 1114–1117 (2012)
Angeline, P., Saunders, G., Pollack, J.: An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans. Neural Netw. 5(1), 54–65 (1994)
Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41–75 (1997)
Chandra, R.: Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction. IEEE Trans. Neural Netw. Learn. Syst. 26, 3123–3136 (2015)
Chandra, R., Frean, M., Zhang, M.: On the issue of separability for problem decomposition in cooperative neuro-evolution. Neurocomputing 87, 33–40 (2012)
Chandra, R., Gupta, A., Ong, Y.-S., Goh, C.-K.: Evolutionary multi-task learning for modular training of feedforward neural networks. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9948, pp. 37–46. Springer, Cham (2016). doi:10.1007/978-3-319-46672-9_5
Chandra, R., Zhang, M.: Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing 186, 116–123 (2012)
Clune, J., Mouret, J.B., Lipson, H.: The evolutionary origins of modularity. In: Proceedings of the Royal Society of London B: Biological Sciences, vol. 280, no. 1755 (2013)
Deo, R., Chandra, R.: Identification of minimal timespan problem for recurrent neural networks with application to cyclone wind-intensity prediction. In: International Joint Conference on Neural Networks (IJCNN), pp. 489–496, July 2016
Evgeniou, T., Micchelli, C.A., Pontil, M.: Learning multiple tasks with kernel methods. J. Mach. Learn. Res. 6(Apr), 615–637 (2005)
Frazier, C., Kockelman, K.: Chaos theory and transportation systems: instructive example. Transp. Res. Rec.: J. Transp. Res. Board 20, 9–17 (2004)
Hansen, N., Müller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 11(1), 1–18 (2003)
Happel, B.L., Murre, J.M.: Design and evolution of modular neural network architectures. Neural Netw. 7(6–7), 985–1004 (1994)
JTWC: Joint Typhoon Warning Center - tropical cyclone best track data site. http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc/ (2017). Accessed 16 Aug 2017
Knaff, J.A., DeMaria, M., Sampson, C.R., Gross, J.M.: Statistical, five-day tropical cyclone intensity forecasts derived from climatology and persistence. Weather Forecast. 18, 80–92 (2003)
Misra, J., Saha, I.: Artificial neural networks in hardware: a survey of two decades of progress. Neurocomputing 74(1–3), 239–255 (2010). Artificial Brains
Morse, A.F., De Greeff, J., Belpeame, T., Cangelosi, A.: Epigenetic robotics architecture (ERA). IEEE Trans. Auton. Mental Dev. 2(4), 325–339 (2010)
Potter, M.A., De Jong, K.A.: Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 8(1), 1–29 (2000)
Potter, M.A., De Jong, K.A.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994). doi:10.1007/3-540-58484-6_269
Prince, C., Helder, N., Hollich, G.: Ongoing emergence: a core concept in epigenetic robotics. In: Proceedings of the Fifth International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems, pp. 63–70. Lund University Cognitive Studies (2005)
Stiles, B.W., Danielson, R.E., Poulsen, W.L., Brennan, M.J., Hristova-Veleva, S., Shen, T.P., Fore, A.G.: Optimized tropical cyclone winds from QuikSCAT: a neural network approach. IEEE Trans. Geosci. Remote Sens. 52(11), 7418–7434 (2014)
Takens, F.: Detecting strange attractors in turbulence. In: Rand, D., Young, L.-S. (eds.) Dynamical Systems and Turbulence, Warwick 1980. LNM, vol. 898, pp. 366–381. Springer, Heidelberg (1981). doi:10.1007/BFb0091924
Zeng, T., Ji, S.: Deep convolutional neural networks for multi-instance multi-task learning. In: 2015 IEEE International Conference on Data Mining (ICDM), pp. 579–588, November 2015
Zheng, H., Geng, X., Tao, D., Jin, Z.: A multi-task model for simultaneous face identification and facial expression recognition. Neurocomputing 171, 515–523 (2016)
Zjavka, L.: Numerical weather prediction revisions using the locally trained differential polynomial network. Expert Syst. Appl. 44, 265–274 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chandra, R. (2017). Dynamic Cyclone Wind-Intensity Prediction Using Co-Evolutionary Multi-task Learning. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_63
Download citation
DOI: https://doi.org/10.1007/978-3-319-70139-4_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70138-7
Online ISBN: 978-3-319-70139-4
eBook Packages: Computer ScienceComputer Science (R0)