PM_2.5 Concentration Prediction Based on PCA-OS-ELM

In order to improve the prediction accuracy of PM_2.5 concentration,a method based on the principal component analysis and online sequential extreme learning machine (PCA-OS-ELM) was proposed to predict PM_2.5 concentration in this paper. Firstly,principal component analysis (PCA) was used to extract the key variables affecting air quality in high-dimensional atmospheric data,and remove unnecessary redundant variables. Secondly,an online sequential extreme learning machine (OS-ELM) network prediction model was established by using the extracted key variables. Finally,the training data and network parameters were continuously updated to realize the rapid prediction of PM_2.5 concentration by combining batch processing with successive iteration. The results show that,taking different batches of training data to update the model,the PCA-OS-ELM prediction method can quickly realize the prediction of atmospheric PM_2.5 concentration,proving the effectiveness of the proposed method. Compared with other methods,this method shows little prediction error,higher prediction accuracy and better practical value.