现代信息科技2024，Vol.8Issue(21)：35-40,45,7.DOI:10.19850/j.cnki.2096-4706.2024.21.008
基于卷积神经网络和双向长短期记忆网络的气温预测模型
Temperature Prediction Model Based on Convolutional Neural Networks and Bidirectional Long Short-Term Memory
摘要
Abstract
There is a nonlinear relationship between temperature and environmental factors.Aiming at the problems that traditional prediction methods are difficult to capture the inherent characteristics and temporal correlation of the data,a temperature prediction model based on a combination of Convolutional Neural Networks and Bidirectional Long Short-Term Memory is proposed.Based on hourly observation data from four national meteorological observation stations in Suqian,firstly,the spatial features of meteorological element data are extracted through the One-dimensional Convolutional Neural Networks,followed by these features are introduced into the Bidirectional Long Short-Term Memory to comprehensively learn and master the contextual information of meteorological elements,so as to effectively predict the temperature.The experimental results show that compared with other prediction methods,this proposed model performs excellently in spatial feature extraction and temporal feature learning,and it has significant advantages in the accuracy of temperature prediction.关键词
深度学习/卷积神经网络/双向长短期记忆网络/气温预测/对比分析Key words
Deep Learning/Convolutional Neural Networks/Bidirectional Long Short-Term Memory/temperature prediction/comparative analysis分类
信息技术与安全科学引用本文复制引用
叶剑,唐欢,殷华,高振翔..基于卷积神经网络和双向长短期记忆网络的气温预测模型[J].现代信息科技,2024,8(21):35-40,45,7.基金项目
江苏省气象局青年基金项目(KQ202420) （KQ202420）
"宿迁英才"群英计划培养资助项目、宿迁市级指导性科技计划项目共同资助. （）
