基于Adam注意力机制的PM_(2.5)浓度预测方法

PM_(2.5) Concentration Prediction Method Based on Adam′s Attention Model

大气PM_(2.5)浓度是一种具有较强时序特征的数据,故目前关于PM_(2.5)浓度的预测多选择RNN、LSTM等序列模型进行。但由于RNN、LSTM等模型对不同时刻输入的数据都采用相同的权重进行计算,不符合类脑设计,造成PM2:5浓度预报准确率较低。针对以上问题,提出一种基于Adam注意力机制的PM_(2.5)预测方法(AT-RNN和AT-LSTM),该方法首先通过Adam算法寻找RNN或LSTM的最优参数并在Encoder阶段引入注意力机制,将注意力权重分配给具有时间序列特征的输入,再进行Decoder解析和预测。通过实验,对比了BP、RNN、LSTM和AT-RNN、AT-LSTM预测合肥市PM2:5浓度的效果。结果表明,基于Adam注意力模型的预测方法准确率优于其它方法,证明该方法在污染物预测中的有效性。

Atmospheric PM_(2.5)concentration is a kind of data with strong time series characteristics,so currently the prediction of PM_(2.5)concentration is mostly based on RNN,LSTM and other sequence models.However,RNN,LSTM and the other similar models use the same weight to calculate the input data at different times,which is not in line with the brain-like design,resulting in the low accuracy of PM_(2.5)concentration prediction.In view of the above problems,a PM_(2.5)prediction method(AT-RNN and AT-LSTM)based on Adam attention mechanism is proposed.This method firstly looks for the optimal parameters of RNN or LSTM through Adam algorithm,and introduces attention mechanism in Encoder stage to assign attention weight to input with time series characteristics,and then carries out Decoder analysis and prediction.Through the experiment,the prediction effects of BP,RNN,LSTM and AT-RNN and AT-LSTM on PM_(2.5)concentration in Hefei city were compared.The results show that the prediction method based on Adam attention model is more accurate than other methods,which proves the effectiveness of this method in pollutant prediction.