Air Pollution Concentration Prediction Method Based on VMD and Combination Model
To improve the accuracy of predicting atmospheric pollutant concentrations,a prediction method based on variational mode decom-position and combination model is proposed.First,the variational mode decomposition reconstructs the historical pollutant concentration data of the target monitoring point into multivariate temporal data,constructs spatiotemporal sequence data based on the geographical relationships be-tween monitoring points in the region;Second,input the processed data into a combination model of LSTM and ConvLSTM to extract both tem-poral and spatial features and output prediction results.Based on the historical concentration data of PM2.5,SO2,and NO2 pollutants in Wuhan City,the proposed prediction method performed the best in MAE,RMSE,and MAPE indicators,significantly outperforming other models.In addition,as the time scale increases,this method still maintains the highest prediction accuracy compared to other models.This method can fully capture local features and has significant advantages in considering both temporal and spatial features,providing a feasible approach for accurate prediction of atmospheric pollutant concentrations.
air pollutionconcentration predictionvariational mode decompositioncombination modelLSTMConvLSTM
万方数据
维普
