基于时空特征提取的空气污染物PM2.5预测

Prediction of Air Pollutant PM2.5 Based on Time-space Feature Extraction

为了充分挖掘多因素数据间的时空特征信息,解决在多种因素相互影响下不能准确预测PM2.5值的问题,提出了一种融合了局部加权回归的周期趋势分解(STL,seasonal-trend decomposition procedure based on loess)算法、卷积长短期记忆网络(ConvLSTM,convolutional long short-term memory network)和门控循环单元(GRU,gated recurrent unit)的PM2.5预测方法;首先利用STL算法将PM2.5数据进行分解,将分解得到的序列分别与其他因素相融合;搭建ConvLSTM-GRU模型,并利用贝叶斯寻优算法进行超参数寻优;将融合数据传入ConvLSTM网络中进行时空特征提取,再将提取后的特征序列传入GRU网络中进行预测;通过与ConvLSTM-GRU模型、CNN-GRU模型以及GRU模型的预测结果进行比较实验,证明所提模型具有误差小、预测效果好等特点。

In order to fully mine the spatiotemporal feature information between multi-factor data,and solve the problem that PM2.5 value cannot be accurately predicted under the influence of multiple factors,a PM2.5 prediction method is proposed to combine with a seasonal-trend decomposition procedure based on Loess(STL)algorithm,convolutional long short-term memory network(ConvLSTM)and gated recurrent unit(GRU).Firstly,the STL algorithm is used to decompose the PM2.5 data and fuse the decomposed sequence with other factors;The ConvLSTM-GRU model is built,and the Bayesian optimization algorithm is used to search for super parameters;The fused data is transferred to extract the ConvLSTM network for time-space feature,and then the extracted feature sequence is transferred to the GRU network for the prediction.Compared with the prediction results of the ConvLSTM-GRU model,CNN-GRU model and GRU model,the results show that the proposed model has the characteristics of small error and good prediction effect.