Two-stage storm entity prediction based on multiscale and attention
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.
attention mechanismdilated convolutionfrequency domain losslong short-term memorytime series prediction
NETL
NSTL
万方数据
