Extended random forest for multivariate air quality forecasting

In this research, an extended random forest algorithm for multivariate time series several steps forecasting is proposed. Peoposed method consists input layer and hidden layers involves random forest. In addition, a new algorithm is proposed in the third step to an ensemble of the tree’s outputs with the concept of correlation with the final results to reduce redundancy. In the output layer, a new algorithm is proposed to learn the weight of each random forest tree to calculate the result. Beijing PM25 and Italian air quality, were used to evaluate the proposed method. The results of the proposed method in this research were compared with the other state-of-the-art methods like deep learning and deep forest. We evaluated our proposed model based on evaluation metrics RMSE, MAE and MAPE and achieved good results. According to the results, the proposed method on the Beijing PM2.5 dataset’s RMSE and MAE value respectively are 40.97 and 24.81 for the average forecast for the next 1–6 h, 2.51 and 0.48 less than the best of the others. The average forecast for the next 1–3 h are 2.71 and 1.42 less than the best value of the others and are equal to 31.64 and 18.09. On the Italian air quality dataset, the RMSE, MAE and MAPE value for the next 1-h forecast are 0.6109, 0.4224 and 33.80 and better than the others.