Typhoon occurrences pose a great threat to people’s lives and property; therefore, it is important to predict typhoon tracks accurately for disaster prevention and reduction. In recent years, research using traditional machine learning methods has struggled to include temporal and spatial features. Moreover, research that has been conducted using satellite images only does not consider the influence of physical factors on typhoon movement; therefore, this paper proposes to add a convolutional layer to the Convolutional LSTM (ConvLSTM) model to improve the ability of the model to extract images. The previous positions of the typhoon’s center are marked on subsequent reanalysis images. The subsequent coordinates of the typhoon’s center are found by fitting the predicted coordinates of each physical variable. The research method in this paper required selecting the physical variables group which was most correlated with the direction and distance of the typhoon movement from 11 physical variables; this was achieved using Canonical Correlation Analysis (CCA) and Grey Relation Analysis (GRA). Then, reanalysis data is transformed into images and a continuous series of reanalysis image sequences is inputted into the ConvLSTM model so that it can make predictions. The mean absolute error of distance used for the ERA5 dataset, using the method proposed, was 54.69 km; thus, the validity of the model was proven.
<abstract><p>Typhoon forecasting has always been a vital function of the meteorological department. Accurate typhoon forecasts can provide a priori information for the relevant meteorological departments and help make more scientific decisions to reduce the losses caused by typhoons. However, current mainstream typhoon forecast methods are very challenging and expensive due to the complexity of typhoon motion and the scarcity of ocean observation stations. In this paper, we propose a typhoon track prediction model, DeepTyphoon, which integrates convolutional neural networks and long short-term memory (LSTM). To establish the relationship between the satellite image and the typhoon center, we mark the typhoon center on the satellite image. Then, we use hybrid dilated convolution to extract the cloud features of the typhoon from satellite images and use LSTM to predict these features. Finally, we detect the location of the typhoon according to the predictive markers in the output image. Experiments are conducted using 13, 400 satellite images of time series of the Northwest Pacific from 1980 to 2020 and 8420 satellite images of time series of the Southwest Pacific released by the Japan Meteorological Agency. From the experimentation, the mean average error of the 6-hour typhoon prediction result is 64.17 km, which shows that the DeepTyphoon prediction model significantly outperforms existing deep learning approaches. It achieves successful typhoon track prediction based on satellite images.</p></abstract>
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