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
于霞 1朱智睿 1段勇 1李冰洁 1杨海波1
作者信息
- 1. 沈阳工业大学 信息科学与工程学院,辽宁 沈阳 110870
- 折叠
摘要
为了提高降水场预测模型的学习效率与预测性能,在预测模型的训练阶段提出一个改善的训练策略,使其可以充分学习物体运动轨迹以及物体运动时的外观变化.通过在一个雷达回波数据集和一个公开数据集上进行对应实验,可以显示出该方法在两项指标的性能表现上具有明显提高,证明了该方法的有效性.
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引用本文复制引用
基金项目
辽宁省教育厅服务地方项目(LJKFZ20220184)
出版年
2024
国家科技期刊平台
NSTL
万方数据