Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model

Abstract

Typhoons are some of the most serious natural disasters, and the key to disaster prevention and mitigation is typhoon level classification. How to better use data of satellite cloud pictures to achieve accurate classification of typhoon levels has become one of classification the hot issues in current studies. A new framework of deep learning neural network, Graph Convolutional–Long Short-Term Memory Network (GC–LSTM), is proposed, which is based on the data of satellite cloud pictures of the Himawari-8 satellite in 2010–2019. The Graph Convolutional Network (GCN) is used to process the irregular spatial structure of satellite cloud pictures effectively, and the Long Short-Term Memory (LSTM) network is utilized to learn the characteristics of satellite cloud pictures over time. Moreover, to verify the effectiveness and accuracy of the model, the prediction effect and model stability are compared with other models. The results show that: the algorithm performance of this model is better than other prediction models; the prediction accuracy rate of typhoon level classification reaches 92.35%, and the prediction accuracy of typhoons and super typhoons reaches 95.12%. The model can accurately identify typhoon eye and spiral cloud belt, and the prediction results are always kept in the minimum range compared with the actual results, which proves that the GC–LSTM model has stronger stability. The model can accurately identify the levels of different typhoons according to the satellite cloud pictures. In summary, the results can provide a theoretical basis for the related research of typhoon level classification.

Classification and Estimation of Typhoon Intensity from Geostationary Meteorological Satellite Images Based on Deep Learning

“…Overall, the 5 × 5 convolution kernel extracted satellite cloud image information more effectively than the other kernels. This result is consistent with that of Zhou et al [12], who found that the 5 × 5 convolution kernel had an obvious feature extraction effect for satellite cloud image characteristics. 2.…”

“…Some progress has also been made in identifying satellite cloud images using different cloud cluster features [10,11]. Zhou et al [12] accurately identified the eye and cloud wall of typhoons and used the GC-LSTM model to accurately recognize and predict typhoon intensity. Zhao et al [13] proposed a real-time typhoon eye detection method based on deep learning with satellite cloud images, which provided important data for detecting real typhoon information.…”

“…Rüttgers et al [16] used past satellite images to generate one-step images. Nevertheless, there are existing issues in terms of low feature extraction of tropical depressions, tropical storms or severe tropical storms and poor recognition accuracy that need to be addressed [12,15,17].…”

Deep fake detection using a sparse auto encoder with a graph capsule dual graph CNN

“…For the number of iterations t , SAE-GLSTM computes the hidden forward sequence as and the hidden backward sequence as , and the output sequence is represented as Y . The input x t has a hierarchical timing layer and the transition of the node state is declared a vector using the standard LSTM ( Zhou, Xiang & Huang, 2020 ). Figure 5 shows the hierarchical forward and backward timing structure of Graph LSTM.…”

A Novel Multi-Input Multi-Output Recurrent Neural Network Based on Multimodal Fusion and Spatiotemporal Prediction for 0–4 Hour Precipitation Nowcasting

“…Precipitation nowcasting can forecast the distribution and development of precipitation in nearby periods with high temporal and spatial resolution. Accurate precipitation nowcasting can not only provide convenience for people's daily life [1,2] but also help in disaster prevention and mitigation [3,4]. The current common operational system for precipitation forecasts is the numerical weather prediction (NWP) [5] model, but it cannot provide accurate nowcasting due to spin-up [6].…”