热带气象学报2024，Vol.40Issue(6)：1063-1073,11.DOI:10.16032/j.issn.1004-4965.2024.093
基于DWT-CNN-LSTM逐日气温预测模型研究
Daily Temperature Prediction Model Based on DWT-CNN-LSTM
摘要
Abstract
Accurate temperature prediction is crucial for human production and life.To address the challenges of traditional temperature prediction methods,which struggle to capture dynamic data changes and often yield poor accuracy,this paper proposed a combined temperature prediction model that integrates discrete wavelet transform(DWT),convolutional neural network(CNN),and long short-term memory network(LSTM).First,the original temperature observation data were decomposed and reconstructed using the DWT.Second,the CNN was used to perform feature extraction,and the LSTM was applied to process the extracted feature information to achieve temperature prediction.Meanwhile,root mean square error(RMSE),mean absolute error(MAE),and coefficient of determination(R2)were used as the evaluation indexes.Lastly,temperature observation data were used to validate the effectiveness of the proposed model,and a comparative analysis was conducted using the LSTM model,the CNN-LSTM model,and the DWT-LSTM model.Experimental results show that,compared with the LSTM model,the CNN-LSTM model,and the LSTM model based on discrete wavelet transform,the DWT-CNN-LSTM model reduced the RMSE by 1.00924,1.00274,and 0.10023,respectively,and the MAE by 0.91836,0.86265,and 0.14489 respectively.It also improved R2 by 0.04703,0.04662,and 0.00400 respectively.These findings confirm the effectiveness of the model in temperature prediction and provide a new reference for temperature prediction,indicating potential for broader future applications.关键词
气温预测/时间序列/离散小波变换/卷积神经网络/长短期记忆网络Key words
temperature prediction/time series/discrete wavelet transform/convolutional neural network/long short-term memory network分类
天文与地球科学引用本文复制引用
樊姝琪,刘慧铭,庄润杰,王诗雨,温永仙..基于DWT-CNN-LSTM逐日气温预测模型研究[J].热带气象学报,2024,40(6):1063-1073,11.基金项目
福建省自然科学基金项目(2021J01126) （2021J01126）
福建农林大学科技创新专项基金(KFb22094XA)共同资助 （KFb22094XA）
