Temperature Prediction Model Based on Convolutional Neural Networks and Bidirectional Long Short-Term Memory
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.
Deep LearningConvolutional Neural NetworksBidirectional Long Short-Term Memorytemperature predictioncomparative analysis
国家科技期刊平台
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