Article Details

Title · Authors · Abstract · Keywords · DOI · References

Estimation of Tropical Cyclone Intensity Based on 3D Convolutional Neural Networks

WANG Yu1, SUN Fengyuan2

(1.Hunan Modern Logistics College, Changsha 410131, China; 2.Unit 75841 of PLA, Changsha 410000, China)

Section：Artificial Intelligence · Vol. 2025, Issue 5 · Pages：51-55,61 · 2096-4706（2025）05-0051-06 · CLC number：TP18；TP391 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2025.05.009

Abstract

In the fields of meteorology and disaster management, the estimation of the TC intensity is of vital significance. With the advancement of technology, methods based on Deep Learning have demonstrated excellent performance in the estimation of the TC intensity, providing strong support for research and practice in related fields. Focusing on the spatiotemporal characteristics of TC, combined with Deep Learning technology, this paper proposes an innovative TC intensity estimation method namely the T3D-Net model. The MAE of this model on the TCIR dataset is 6.92 kt, and the RMSE is 9.14 kt. Compared with multiple existing methods for estimating the TC intensity, this method exhibits a certain competitiveness and superiority.

Keywords

estimation of intensity of TC; 3D Convolutional Neural Networks; TCIR; spatiotemporal feature

References

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About the authors

Wang Yu (1998-), female, Han ethnicity, from Cangzhou, Hebei Province, is a master's student with research interests in deep learning and image processing.