基于多尺度与注意力机制的两阶段风暴单体外推研究  

Two-stage storm entity prediction based on multiscale and attention

作　　者：魏敏[1] 姚鑫 WEI Min;YAO Xin(School of Computer Science,Chengdu University of Information Technology,Chengdu Sichuan 610225,China)

机构地区：[1]成都信息工程大学计算机学院,四川成都610225

出　　处：《图学学报》2024年第4期696-704,共9页Journal of Graphics

基　　金：四川省科技计划项目(2023YFQ0072)。

摘　　要：风暴是一种生命周期短、发生突然、空间尺度小的自然现象,常用雷达回波外推方法进行预测,但时序预测模型难以在众多特征中定位风暴关键信息,导致预测精度低,模型无法充分学习图像高频信息,导致预测细节缺失,结果模糊。为了提升预测性能,提出两阶段风暴单体外推框架。第一阶段使用多尺度模块提取多尺度信息,注意力机制挖掘影响预测的重要特征,使用时空长短期记忆单元进行序列预测。第二阶段对一阶段结果进行偏差矫正,使用频域损失丰富外推细节。实验结果表明,在雷达回波数据集上,与主流模型PredRNN-V2相比,该模型均方误差降低11.4%,SSIM提升4.3%,在风暴单体外推任务中表现优越。在Moving MNIST数据集上,均方误差降低4.95%,感知损失降低12.67%,SSIM提升至0.898,具有良好的时序预测能力。Storms are a type of natural phenomenon characterized by a short life cycle,sudden occurrence,and small spatial scale.Radar echo backpropagation methods are commonly employed for prediction.However,time series prediction models find it difficult to locate the key information of storms among numerous features,leading to low prediction accuracy.The models cannot fully learn the high-frequency information in images,resulting in missing details in the predictions and blurry results.To enhance prediction performance,we proposed a two-stage framework for single storm forecasting.In the first stage,a multi-scale module extracted multi-scale information,while an attention mechanism mined important features impacting prediction.Spatiotemporal long-term and short-term memory units were utilized for sequence prediction.The second stage performed bias correction on the results of the first stage.Frequency domain loss enriched prediction details.Experimental results showed that on the radar echo dataset,compared with the mainstream PredRNN-V2 model,the mean squared error was reduced by 11.4%and SSIM was improved by 4.3%,showing superior performance in single storm forecasting tasks.On the Moving MNIST dataset,the mean squared error was reduced by 4.95%,the perceptual loss was reduced by 12.67%,and the SSIM was improved to 0.898,demonstrating strong time series prediction capabilities.

关 键 词：注意力机制 空洞卷积 频域损失 长短期记忆 时空序列预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN957[自动化与计算机技术—计算机科学与技术] P412[电子电信—信号与信息处理]