## 1 Introduction
There are also studies on choosing the location of noise monitoring sites to make the monitoring more effective [21]. In general, there are few studies focusing on the temporal variation of urban noise using physical propagation models [18; 19; 20; 21]. The second group of noise prediction methods is the traditional statistical method. [PERSON] and [PERSON] [22] proposed an autoregressive integrated moving average (ARIMA) model to predict traffic noise time series, but the amount of data was small, and the monitoring time was only a few hours. It is also concluded that it is better to consider the time series on a longer time scale. [PERSON] et al. [23] analyzed the long-term noise monitoring data through ARIMA modeling technology and found that the ARIMA method is reliable for time series modeling of traffic noise, but the corresponding parameters should be adjusted according to different situations. [PERSON] et al. [24] performed a Fourier analysis of the traffic noise of the two cities of Caceres and Talca, and discovered that regardless of the measurement environment of the city and the corresponding traffic flow, larger periodic components and amplitude values are similar in different samples, which indicated that urban traffic noise has an inherent law that can be predicted. There is also a study that developed models based on the regression model to prognosticate the level of noise in closed industrial spaces through the dominant frequency cutoff point [25]. The third group of noise prediction methods is the machine learning method. [PERSON] et al. [26] introduced the genetic algorithm into the traffic noise prediction and proposed two prediction models, and the simulation on the actual data achieved an accuracy of \(\pm 1\%\). [PERSON] et al. [27] occupied a back propagation neural network to simulate traffic noise. [PERSON] et al. [28] used a random forest regression method to predict wind turbine noise. [PERSON] and [PERSON] [29] used the feature extraction and machine learning methods to predict the environmental noise and got an excellent fitting effect. However, the number of input variables was 32, the distinction is subtle, and it is challenging to organize the data. The prediction of environmental noise heavily depends on historical and real-time noise monitoring data. The research by [PERSON] et al. [30] shows that combining the rules or patterns mined in the monitoring data with the acoustic theoretical calculation model can effectively improve the prediction accuracy of noise. In terms of reflecting regional noise levels, sometimes sampling strategies are put forward based on considerations of saving resources and improving data acquisition efficiency [31]. [PERSON] et al. [32] found that considering saving the time cost and the accuracy of the results, a non-continuous 5-7 days observation of noise is reasonable for long-term noise prediction. Forecasting the temporal variation of noise can offer a scientific basis for urban noise control. In recent years, with the widespread utilization of sound level meters and the development of various sensor network technologies, environmental noise data has an exploding expansion. Although there have been previous studies on noise measurement, prediction, and control [19; 22; 23; 25; 33; 34; 35; 36], most of the research data are relatively diminutive. This gave the inspiration to have a second thought about the environmental noise prediction problem, that is, whether there are more optimized noise prediction models and methods when handling an abundant amount of noise data. Therefore, predicting noise in the time dimension requires a more efficient approach. However, at a fine time interval, few studies have focused on and predicted the variation of noise within a given day so far. Deep learning has developed rapidly and has been successfully applied in many specialties lately [37].