基于PCA-OS-ELM的大气PM2.5浓度预测

为了提高细颗粒物PM_2.5浓度预测精度，提出一种主元成分分析与在线序列极限学习机相结合（PCA-OS-ELM）的PM_2.5浓度预测方法. 首先，通过主成分分析方法（PCA）提取高维大气数据中影响空气质量的关键变量，并去除不必要的冗余变量；其次，利用提取的关键变量建立在线序列极限学习机（OS-ELM）网络预测模型，将批处理和逐次迭代相结合，不断更新训练数据和网络参数实现大气PM_2.5浓度快速预测.研究结果表明，PCA-OS-ELM预测方法采用不同批次训练数据更新模型的方式，能够快速实现大气PM_2.5浓度预测，证明了该方法的有效性.与其他方法相比，该方法预测误差小，预测精度高，具有更好的实用价值. 

PM2.5 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.