JACIII Vol.22 No.4 pp. 465-474
doi: 10.20965/jaciii.2018.p0465


Time Series Prediction of Tropical Storm Trajectory Using Self-Organizing Incremental Neural Networks and Error Evaluation

Wonjik Kim* and Osamu Hasegawa**,***

*Department of Systems and Control Engineering, Tokyo Institute of Technology
2-12-1-S5-22 Ookayama, Meguro-ku, Tokyo 152-8550, Japan

**Department of Systems and Control Engineering, Tokyo Institute of Technology
4259-J3-13 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan

***SOINN Inc.
Cureindo-building 405, 8-4-30 Tsuruma, Machida, Tokyo 194-0004, Japan

October 25, 2017
April 3, 2018
July 20, 2018
tropical storm, natural disaster, route forecasting, neural network, artificial intelligence
Time Series Prediction of Tropical Storm Trajectory Using Self-Organizing Incremental Neural Networks and Error Evaluation

Tropical route prediction algorithm

This study proposes a route prediction method using a self-organizing incremental neural network. The route trajectory is predicted from two location parameters (the latitude and longitude of the middle of a tropical storm) and the meteorological information (the atmospheric pressure). The method accurately predicted the normalized atmospheric pressure data of East Asia in the topological space of latitude and longitude, with low calculation cost. This paper explains the algorithms for training the self-organizing incremental neural network, the procedure for refining the datasets and the method for predicting the storm trajectory. The effectiveness of the proposed method was confirmed in experiments. With the results of experiments, possibility of prediction model improvement is discussed. Additionally, this paper explains the limitations of proposed method and brief solution to resolve. Although the proposed method was applied only to typhoon phenomena in the present study, it is potentially applicable to a wide range of global problems.

Cite this article as:
W. Kim and O. Hasegawa, “Time Series Prediction of Tropical Storm Trajectory Using Self-Organizing Incremental Neural Networks and Error Evaluation,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.4, pp. 465-474, 2018.
Data files:
  1. [1] C. C. Fair et al., “Natural Disasters and Political Engagement: Evidence from the 2010–11 Pakistani Floods,” Stanford University Graduate School of Business Research Paper, No.17-42, 2017.
  2. [2] H. Toya and M. Skidmore, “Economic Development and the Impacts of Natural Disasters,” Economics Letters, Vol.94, Issue 1, pp.20-25, 2007.
  3. [3] S. Ponserre et al., “Annual disaster statistical review 2011: the numbers and trends,” Centre for Research on the Epidemiology of Disasters (CRED), 2012.
  4. [4] C. M. Mabry et al., “Typhoon Disturbance and Stand-level Damage Patterns at a Subtropical Forest in Taiwan,” Biotropica, Vol.30, No.2, pp. 238-250, 1998.
  5. [5] R. A. Pielke Jr. et al., “Normalized hurricane damage in the United States: 1900–2005,” Natural Hazards Review, Vol.9, No.1, pp. 29-42, 2008.
  6. [6] K. Emanuel, “Increasing destructiveness of tropical cyclones over the past 30 years,” Nature, Vol.436, pp. 686-688, 2005.
  7. [7] J. Zschau and A. N. Küppers, “Early warning systems for natural disaster reduction,” Springer Science & Business Media, 2013.
  8. [8] X.-J. Yang et al., “The TianHe-1A supercomputer: its hardware and software,” J. of Computer Science and Technology, Vol.26, Issue 3, pp. 344-351, 2011.
  9. [9] National Ocean Service Homepage, [accessed May 30, 2017]
  10. [10] Japan Meteorological Agency Webpage of offering typhoon data, [accessed April 21, 2017]
  11. [11] Japan Weather Association’s information Homepage, [accessed May 21, 2017]
  12. [12] C.-C. Wu et al., “The impact of dropwindsonde data on typhoon track forecasts in DOTSTAR,” Weather and Forecasting, Vol.22, No.6, pp. 1157-1176, 2007.
  13. [13] H. Zhang et al., “Application of direct assimilation of ATOVS microwave radiances to typhoon track prediction,” Advances in Atmospheric Sciences, Vol.21, Issue 2, pp. 283-290, 2004.
  14. [14] H. C. Weber, “Hurricane track prediction using a statistical ensemble of numerical models,” Monthly Weather Review, Vol.131, No.5, pp. 749-770, 2003.
  15. [15] R. T. Lee and J. K. Liu, “Tropical cyclone identification and tracking system using integrated neural oscillatory elastic graph matching and hybrid RBF network track mining techniques,” IEEE Trans. on Neural Networks, Vol.11, No.3, pp. 680-689, 2000.
  16. [16] T.-L. Lee, “Back-propagation neural network for the prediction of the short-term storm surge in Taichung harbor, Taiwan,” Engineering Applications of Artificial Intelligence, Vol.21, No.1, pp. 63-72, 2008.
  17. [17] M. M. Ali et al., “A neural network approach to estimate tropical cyclone heat potential in the Indian Ocean,” IEEE Geoscience and Remote Sensing Letters, Vol.9, No.6, pp. 1114-1117, 2012.
  18. [18] S. Furao and O. Hasegawa “An incremental network for on-line unsupervised classification and topology learning,” Neural Networks, Vol.19, pp. 90-106, 2006.
  19. [19] Japan Meteorological Agency Webpage of information about Japan’s traditional atmospheric pressure, [accessed July 9, 2017]
  20. [20] M. Gidea and Y. Katz., “Topological Data Analysis of Financial Time Series: Landscapes of Crashes,” arXiv:1703.04385, 2017.
  21. [21] C. Lepetit et al., “Topological analysis of the metal-metal bond: a tutorial review,” Coordination Chemistry Reviews, Vol.345, pp. 150-181, 2017.
  22. [22] Y. Umeda, “Time Series Classification via Topological Data Analysis,” Trans. of the Japanese Society for Artificial Intelligence, Vol.32, No.3, p. D-G72_1-12, 2017.
  23. [23] Z.-K. Gao, M. Small, and J. Kurths, “Complex network analysis of time series,” EPL (Europhysics Letters), Vol.116, No.5, 2017.
  24. [24] J. M. Zurada, “Introduction to Artificial Neural Systems,” Jaico Publising House, 1994.
  25. [25] K. Yamasaki et al., “Self-Organizing Incremental Neural Network –SOINN– and Its Usage,” Japan Neural Network Society, Vol.17, Issue 4, pp. 187-196, 2010.
  26. [26] T. Kohonen, “The self-organizing map,” Neurocomputing, Vol.21, No.1, pp. 1-6, 1998.
  27. [27] T. Martinetz and K. Schulten, “A “neural-gas” network learns topologies,” Artificial Neural Networks, pp. 397-402, Elsevier Science Publishers, 1991.
  28. [28] B. Fritzke, “A growing neural gas network learns topologies,” Advances in Neural Information Processing Systems, pp. 625-632, 1995.
  29. [29] Y. Wu and M. Takatsuka, “Spherical self-organizing map using efficient indexed geodesic data structure,” Neural Networks, Vol.19, No.6, pp. 900-910, 2006.
  30. [30] X. Xiong, H. Zhang, and O. Hasegawa, “Density estimation method based on self-organizing incremental neural networks and error estimation,” Int. Conf. on Neural Information Proc., Springer, 2013.
  31. [31] M. A. Bender et al., “Impact of storm size on prediction of storm track and intensity using the 2016 Operational GFDL Hurricane Model,” Weather and Forecasting, Vol.32, No.4, pp. 1491-1508, 2017.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Aug. 16, 2018