Accurate predictions of hourly PM_(2.5)concentrations 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 PM_(2.5)concentrations 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 PM_(2.5)concentrations.Taking the PM_(2.5)concentration 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 PM_(2.5)concentration and can be employed as a meaningful PM_(2.5)concentrations prediction tool.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.:71874133 and 72201201)the Research Program of Shaanxi Soft Science,China(Grant No.:2022KRM015)+1 种基金the Youth Innovation Team of Shaanxi Universities(2020-68)Shaanxi Province Qin Chuangyuan“scientist t engineer”team building project(Grant No.:2022KXJ-007).
文摘Accurate predictions of hourly PM_(2.5)concentrations 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 PM_(2.5)concentrations.Taking the PM_(2.5)concentration 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 PM_(2.5)concentration and can be employed as a meaningful PM_(2.5)concentrations prediction tool.
