基于Adam注意力机制的PM2.5浓度预测方法

大气PM2.5浓度是一种具有较强时序特征的数据,故目前关于PM2.5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PM2.5预测方法(AT-RNN和...

PM2.5 Concentration Prediction Method Based on Adam's Attention Model

Atmospheric PM_2.5concentration is a kind of data with strong time series characteristics,so currently the prediction of PM_2.5concentration 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_2.5concentration prediction.In view of the above problems,a PM_2.5prediction 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_2.5concentration 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.