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Forecasting hourly PM2.5concentrations based on decomposition-ensemble-reconstruction framework incorporating deep learning algorithms 认领 引用 被引量：2
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作者 Peilei Cai Chengyuan Zhang Jian Chai 《Data Science and Management》 2023年第1期46-54,共9页
Accurate predictions of hourly PM2.5concentrations are crucial for preventing the harmful effects of air pollution.In this study,a new decomposition-ensemble framework incorporating the variational mode decompositi... Accurate predictions of hourly PM2.5concentrations are crucial for preventing the harmful effects of air pollution.In this study,a new decomposition-ensemble framework incorporating the variational mode decomposition method(VMD),econometric forecasting method(autoregressive integrated moving average model,ARIMA),and deep learning techniques(convolutional neural networks(CNN)and temporal convolutional network(TCN))was developed to model the data characteristics of hourly PM2.5concentrations.Taking the PM2.5concentration of Lanzhou,Gansu Province,China as the sample,the empirical results demonstrated that the developed decomposition-ensemble framework is significantly superior to the benchmarks with the econometric model,machine learning models,basic deep learning models,and traditional decomposition-ensemble models,within one-,two-,or three-step-ahead.This study verified the effectiveness of the new prediction framework to capture the data patterns of PM2.5concentration and can be employed as a meaningful PM2.5concentrations prediction tool. 展开更多
关键词 PM2.5concentration prediction Decomposition-ensemble-reconstruction framework Variational mode decomposition method Deep learning
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