沈阳大学学报（自然科学版）2024，Vol.36Issue(2)：121-131,11.
基于雷达回波进行降水场预测的无监督学习模型训练策略
Unsupervised Learning Model Training Strategy for Precipitation Field Prediction Based on Radar Echoes
摘要
Abstract
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.关键词
机器学习/深度学习/降水预测/循环神经网络/帧预测Key words
machine learning/deep learning/precipitation prediction/recurrent neural network/frame prediction分类
信息技术与安全科学引用本文复制引用
于霞,朱智睿,段勇,李冰洁,杨海波..基于雷达回波进行降水场预测的无监督学习模型训练策略[J].沈阳大学学报（自然科学版）,2024,36(2):121-131,11.基金项目
辽宁省教育厅服务地方项目(LJKFZ20220184). （LJKFZ20220184）
