摘要
在气象学和灾害管理领域,热带气旋(Tropical Cyclone,TC)的强度估测具有至关重要的意义。随着科技的进步,基于深度学习的方法在热带气旋强度估测上展现出了卓越的性能,为相关领域的研究和实践提供了有力支持。文章聚焦于热带气旋的时空特征,并结合深度学习技术提出了一种创新的TC强度估测方法—Time-space 3D Network(T3D-Net)模型。该模型在TCIR数据集上的MAE为6.92 kt,RMSE为9.14 kt,与现有的多个热带气旋强度估测方法相比,该方法展现出了一定的竞争性和优越性。
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.
作者
王瑜
孙凤远
WANG Yu;SUN Fengyuan(Hunan Modern Logistics College,Changsha 410131,China;Unit 75841 of PLA,Changsha 410000,China)
出处
《现代信息科技》
2025年第5期51-55,61,共6页
Modern Information Technology
关键词
热带气旋强度估测
3D卷积神经网络
TCIR
时空特征
estimation of intensity of TC
3D Convolutional Neural Networks
TCIR
spatiotemporal feature
作者简介
王瑜(1998-),女,汉族,河北沧州人,硕士研究生,研究方向:深度学习、图像处理。
