Bagging and boosting techniques in prediction of particulate matters

The paper presents new ensemble solutions, which can forecast the average level of particulate matters PM10 and PM2.5 with increased accuracy. The proposed network is composed of weak predictors integrated into a final expert system. The members of the ensemble are built based on deep multilayer perceptron and decision tree and use bagging and boosting principle in elaborating common decisions. The numerical experiments have been carried out for prediction of daily average pollution of PM10 and PM2.5 for the next day. The results of experiments have shown, that bagging and boosting ensembles employing these weak predictors improve greatly the quality of results. The mean absolute errors have been reduced by more than 30% in the case of PM10 and 20% in the case of PM2.5 in comparison to individually acting predictors.