WANG Dongmeng,WEN Bin,LI Xiaoyan,et al.PCC-DBN-LSTM Temperature Prediction Model based on Improved Sparrow Algorithm[J].Journal of Chengdu University of Information Technology,2024,39(05):527-533.[doi:10.16836/j.cnki.jcuit.2024.05.002]
基于改进麻雀算法的PCC-DBN-LSTM气温预测模型
- Title:
- PCC-DBN-LSTM Temperature Prediction Model based on Improved Sparrow Algorithm
- 文章编号:
- 2096-1618(2024)05-0527-07
- Keywords:
- temperature prediction; pearson product-moment correlation coefficient; deep confidence network; improving the sparrow algorithm; long short-term memory network
- 分类号:
- TP389.6
- 文献标志码:
- A
- 摘要:
- 气温预测是气象学中的一个重要研究领域。随着气象精准化发展,迫切需要提升气温预测的精准度。为解决传统气温预测算法效果不佳,并且对于多个站点气象数据时空特征提取能力不足,提出一种基于改进麻雀算法优化的皮尔逊积矩相关系数(PCC)-深度置信网络(DBN)-长短时记忆网络(LSTM)的气温预测模型。首先利用Pearson相关系数对众多的气象参数进行选择,DBN网络对输入的多站点气象数据特征进行提取和降维,LSTM对提取的特征进行建模和预测。由于模型初始化参数众多,提出改进麻雀算法优化DBN-LSTM网络参数,提高模型的预测精度和稳定性。实验表明:所提模型的RMSE为0.527,精度高于单一模型和同类模型。
- Abstract:
- Temperature prediction is an important research field in meteorology. With the development of meteorological technology, there is a need to improve the accuracy of temperature predictionurgently. To solve the poor effect of traditional temperature prediction algorithms and the insufficient ability to extract the spatiotemporal characteristics of meteorological data from multiple stations, a multi-station temperature prediction model based on the Pearson product-moment correlation coefficient(PCC)-Deep Belief Network(DBN)-Long Short-Term Memory Network(LSTM)optimized by the improved Sparrow algorithm was proposed. Firstly, Pearson correlation coefficients are used to select numerous meteorological parameters, DBN networks extract and reduce the dimensionality of input multi-site meteorological data, and LSTM modelspredict the extracted features. Due to the numerous initialization parameters of the model, an improved sparrow algorithm is proposed to optimize the parameters of the DBN-LSTM network, improving the prediction accuracy and stability of the model. Experiments show that the RMSE of the proposed model is 0.527, which is lower than that ofa single modeland other similar models.
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备注/Memo
收稿日期:2023-06-06
基金项目:四川省科学技术厅重点研发资助项目(2023YFN0051)
通信作者:李晓燕.E-mail:1036969486@qq.com