Noise Map within Beijing's Fifth Ring Road Based on Random Forest Model
By combining environmental noise routine monitoring data with road traffic,points of interest,natural and social economic factors,meteorology,air pollution,time variables,etc.,a random forest(RF)model was used to create noise maps and evaluate the spatiotemporal distribution characteristics of noise within Beijing's 5th Ring Road.The sliding windows sequential forward selection(SWSFS)was used to select the optimal parameter set.The model's variable importance ordering was used to explore the influencing factors of noise.The results showed that the noise intensity detected by the routine monitoring of the acoustic environment within Beijing's 5th Ring Road was 56.71 dB(A)±9.83 dB(A)in 2019.The weighted noise intensity predicted by RF model was 59.87 dB(A)±6.41 dB(A),and the noise in late night was lower than in day and night.The model validation results indicated that the model had good predictive performance,with a ten-fold cross-validation R2 of 0.78,root mean square error(RMSE)of 4.65 dB(A),and mean absolute error(MAE)of 3.60 dB(A).Compared with the Land Use Regression(LUR)model,the R2of the RF model increased by 35.09％,whereas the RMSE and MAE decreased by 24.13％and 23.46％,respectively.The RF model's variables importance ordering showed that road traffic,especially main roads with busy traffic flow,points of interest,especially bus stops,restaurants,shopping places,and time periods,were the main factors affecting the spatial variation of noise.The RF model can be a robust method for reflecting noise variability in megacities such as Beijing and may provide an efficient solution for noise exposure assessment.
NETL
NSTL
万方数据
