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

Wonjik Kim, Osamu Hasegawa

Journal of Advanced Computational Intelligence and Intelligent Informatics · 19 July 2018

In one sentence. Self-organising incremental neural network predicts tropical storm tracks from centre latitude, longitude and pressure over East Asia, with low computational cost.

Abstract

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.

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