XGBoost-LSTM变权组合模型支持下短期PM<sub>2.5</sub>浓度预测——以上海为例

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Abstract In order to further improve the accuracy of PM_2.5 concentration prediction, a variable weight combination short-term 1-hour PM_2.5 concentration prediction model based on LSTM network and XGBoost model was proposed. First, analyze the predictive factors, explore the influence of air pollutant factors and meteorological factors on the PM_2.5 concentration, to determine the best PM_2.5 concentration predictive factors and analysis the variable importance. Then, after data pretreatment the LSTM prediction model and the XGBoost prediction model was built respectively, and adopt the adaptive variable weight combination method based on residual improvement to obtain the final prediction result. The results show that:The relative importance of pollutant variables is higher than the importance of meteorological factors, among which the relative importance of current PM_2.5 concentration and CO concentration is higher, while the importance of average wind speed and relative humidity is lower. The values of RMSE, MAE and MAPE of the variable weight combined XGBoost-LSTM (Variable) model proposed in this study are 1.75, 1.12 and 6.06, which are better than LSTM, XGBoost, SVR, XGBoost-LSTM (Equal) and XGBoost-LSTM (Residual) model. The combined model predicts performance best in spring but the forecast accuracy is poor in summer. The variable weight method combination model proposed in this study effectively combines the advantages of the two models, not only considers the time series information of the data but also takes into account the nonlinear relationship between the features, and has higher prediction accuracy compared with other models.

Key words： long short term memeny (LSTM) XGBoost PM_2.5 forecast variable weight combination model

Received: 26 January 2021

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X831

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	KANG Jun-feng
	TAN Jian-lin
	FANG Lei
	XIAO Ya-lai

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KANG Jun-feng,TAN Jian-lin,FANG Lei等. Short-term PM_2.5 concentration prediction based on XGBoost and LSTM variable weight combination model: a case study of Shanghai[J]. CHINA ENVIRONMENTAL SCIENCECE, 2021, 41(9): 4016-4025.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2021/V41/I9/4016

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