Nonlinear Autoregressive Network with the Use of a Moving Average Method for Forecasting Typhoon Tracks

Kerh, Tienfuan; Wu, Shin-Hung

doi:10.5121/ijaia.2017.8605

Cited by 1 publication

(1 citation statement)

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“…Gao et al [19] built a typhoon path prediction model based on LSTM (Long Short-Term Memory) and found that its error size is affected by the amount of data set and is more suitable for making short-term predictions of typhoon paths. Tienfuan Kerh et al [20] proposed a method that combines static and dynamic neural network models and treats the variables affecting the typhoon path as a time series and uses a nonlinear autoregressive network with a moving average method to predict the typhoon track, which provides a new idea and method for typhoon path prediction. In the literature [21], a classification and prediction method based on a gated unit recurrent neural network was proposed, and he first classified the typhoon using a dynamic regularization algorithm, and then the typhoon path was predicted using GRU.…”

Section: Introductionmentioning

confidence: 99%

Typhoon Track Prediction Based on Deep Learning

Ren

Cui

2022

Applied Sciences

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China is located in the northwest Pacific region where typhoons occur frequently, and every year typhoons make landfall and cause large or small economic losses or even casualties. Therefore, how to predict typhoon paths more accurately has undoubtedly become an important research topic nowadays. Therefore, this paper predicts the path of typhoons formed in the South China Sea based on deep learning. This paper combines the CNN network and the LSTM network to build a C-LSTM typhoon path prediction model, using the typhoon paths and related meteorological variables formed in the South China Sea from 1949 to 2021 as the data set, and using the Granger causality test to select multiple features for the data set to achieve data dimensionality reduction. Finally, by comparing the experiments with the LSTM typhoon path prediction model, it is proved that the prediction results of the model have smaller errors.

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Section: Introductionmentioning

confidence: 99%