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

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.

Tropical route prediction algorithm

Tropical route prediction algorithm

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.
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Last updated on Apr. 22, 2024