Typhoon Track Prediction Based on Deep Learning
In one sentence. A CNN-LSTM hybrid (C-LSTM) predicts South China Sea typhoon tracks using Granger-selected meteorological features, showing smaller errors than a plain LSTM.
Abstract
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.
Labels
- Topic
- Extremes & hazards
- Type
- New model or method
- Method
- CNN / U-Net, RNN / LSTM / ConvLSTM
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.3390/app12168028 OpenAlex:W4291016023
- Added
- 2026-10-22
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