Unsupervised Learning Model Training Strategy for Precipitation Field Prediction Based on Radar Echoes
In order to enhance the learning efficiency and predictive performance of precipitation field forecasting models,an improved training strategy during the training phase of the prediction model was proposed.This strategy enabled the model to fully learn the trajectories of object movements as well as the appearance changes of objects during movement.Through corresponding experiments conducted on a radar echo dataset and a publicly available dataset,it was demonstrated that this method could significantly improved the performance on two metrics,thus validating its effectiveness.
国家科技期刊平台
NSTL
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