Deep Learning for Typhoon Intensity Classification Using Satellite Cloud Images
Abstract
A tropical cyclone, also known as a typhoon, is one of the most destructive weather phenomena. Its intense cyclonic eddy circulations often cause serious damage to coastal areas. Accurate classification or prediction for typhoon intensity is crucial to disaster warning and mitigation management. But typhoon intensity-related feature extraction is a challenging task as it requires significant preprocessing and human intervention for analysis, and its recognition rate is poor due to various physical factors such as tropical disturbance. In this study, we built a Typhoon-CNNs framework, an automatic classifier for typhoon intensity based on a convolutional neural network (CNN). The Typhoon-CNNs framework utilized a cyclical convolution strategy supplemented with dropout zero-set, which extracted sensitive features of existing spiral cloud bands (SCBs) more effectively and reduces the overfitting phenomenon. To further optimize the performance of Typhoon-CNNs, we also proposed the improved activation function (T-ReLU) and the loss function (CE-FMCE). The improved Typhoon-CNNs was trained and validated using more than 10 000 multiple sensor satellite cloud images from the National Institute of Informatics. The classification accuracy reached to 88.74%. Compared with other deep learning methods, the accuracy of our improved Typhoon-CNNs was 7.43% higher than ResNet50, 10.27% higher than InceptionV3, and 14.71% higher than VGG16. Finally, by visualizing hierarchic feature maps derived from Typhoon-CNNs, we can easily identify the sensitive characteristics such as typhoon eyes, dense-shadowing cloud areas, and SCBs, which facilitate classifying and forecasting typhoon intensity.
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