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文章摘要
基于小波分析优化PM2.5浓度预测模型
Wavelet Analysis for Optimizating PM2.5 Concentration Prediction Models
  
DOI:
中文关键词: PM2.5  气象要素  多元线性回归  BP神经网络  小波分析
英文关键词: PM2.5  Meteorological elements  Multiple linear regression  BP neural network  Wavelet analysis
基金项目:广西气象科研基金资助项目(桂气科 2019M22);贵港市科技局基金资助项目(贵科攻 1505004)
作者单位
许艺馨 广西物流职业技术学院 
任杰 南京理工大学环境科学与工程学院 
冯磊 广西物流职业技术学院 
梁莹露 贵港市气象局 
刘怡明 贵港市气象局 
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中文摘要:
      采用多元线性回归方法(MLR)和BP神经网络方法(BPNN),按1 h、3 h、6 h、12 h、24 h、48 h预测时长对贵港市2015—2018年PM2.5浓度建模并检验对比模型准确率。结果表明,基于MLR与BPNN都能对PM2.5浓度作预测,预测效果随着预测时长的增加而下降,MLR、BPNN模型预测结果平均绝对误差(MAE)分别为401 μg/m3~1548 μg/m3、389 μg/m3~1563 μg/m3。采用小波分析方法对污染物数据优化并再次建模,结果表明,小波-多元线性回归(W MLR)模型与小波-神经网络(W BPNN)模型均得到优化,3 h~24 h预测时长优化效果尤为显著,W MLR、W BPNN模型预测结果分别使MAE降低16%~135%、08%~98%,且后者预测效果优于前者。
英文摘要:
      Using multiple linear regression method (MLR) and BP neural network method (BPNN), PM2.5 concentration prediction models were built by taking 1 h, 3 h, 6 h, 12 h, 24 h, 48 h as prediction time from 2015 to 2018 in Guigang. The accuracy of the models were tested and compared. The results showed that both MLR and BPNN could be used in PM2.5 concentration prediction. The prediction accuracy declined as the prediction time increased. The mean absolute error(MAE) of the prediction models by MLR and BPNN were 4.01 μg/m3~15.48 μg/m3 and 3.89 μg/m3~15.63 μg/m3, respectively. Using wavelet data analysis method for optimizing the contaminant data and making the model again, both W MLR and W BPNN were optimized. The optimization was significant when the prediction time was from 3 h to 24 h. By W MLR and W BPNN models, the MAE reduced 1.6%~13.5% and 0.8%~9.8%, respectively, and W BPNN model was superior to W MLR model in prediction.
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