Prediction of road carbon emission concentration based on recurrent neural network model
The article took the chronological data of CO2 concentration in the basic road section of Yongzhou Avenue in Yongzhou City,Hunan Province as the research object,aiming to realize the real-time prediction of CO2 concentration in road sections.The CO2 concentration data of road sections for model training and prediction accuracy calculation were measured,and the data were smoothed and denoised by Savitzky-Golay filter.On the basis of debugging and establishing the optimal model structure of recurrent neural network,multivariate prediction models(MLR,SVR,BP)and time-sequence prediction models(BP,RF,RNN,LSTM,GRU)were introduced for comparison of the prediction performance in order to provide references to real-time prediction of road section CO2 concentration,the results showed that time-sequence prediction model has better prediction effect compared with multivariate prediction model.The results show that the time-series prediction model has better prediction results than the multivariate prediction model,especially,the GRU in the recurrent neural network model shows higher prediction accuracy,followed by LSTM,and finally RNN.The article concludes that the recurrent neural network model has an outstanding performance in the task of training and prediction of chronological data of roadway section CO2 concentration,and it is able to predict the CO2 concentration of roadway sections in real time and with high accuracy.
万方数据
维普
