## 2 Materials and Methods
### Network Configuration and Training  
The root mean square error (RMSE) and the mean absolute error (MAE) are selected as the evaluation criteria for the performance of the model. The calculation formulas are  
\[\text{RMSE}=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}} \tag{7}\]  
\[\text{MAE}=\frac{1}{m}\sum_{i=1}^{m}\bigl{|}((y_{i}-\hat{y}_{i})\bigr{|} \tag{8}\]  
where \(m\) represents the length of the test sequence, \(y_{i}\) is the actual observation, \(\hat{y}_{i}\) is the predicted value, and smaller RMSE and MAE represent better prediction accuracy of the model.  
Determining critical hyperparameters is significant in building a short-term noise prediction model based on LSTM [50]. According to the study by [PERSON] et al., the interaction of hyperparameters in LSTM is minute [51], which means that each hyperparameter can be independently adjusted. The purpose of the tuning is to find the right parameters, rather than producing the final model. Generally, parameters that perform well on small data sets also perform satisfactorily on large data sets. To enhance the speed of the parameter adjustment, we used a streamlined data set, that is, the 1-min time interval was applied to average the noise monitoring data per minute, thereby compressing the length of the noise time series to 1/60 of the original.  
Learning rate, number of hidden layer neurons, batch size and dropout are critical parameters of the model. 80% of the data is used as a training set, and 20% of the data is used as a test set in this study. In the network configuration process, only RMSE is used to evaluate the performance of the model.  
The learning rate \(\eta\) has an essential influence on the performance of the model and is a relatively important parameter in the model. This study uses the hyperparameter optimization algorithm to optimize the learning rate. The commonly used adaptive learning rate optimization algorithms are AdaGrad, RMSProp, Adam, etc. [52]. Among them is the Adam algorithm, which combines the advantages of Adagrad to deal with sparse gradients and RMSprop to deal with non-stationary targets. It has little memory requirement and can calculate different adaptive learning rates for different parameters. Adam is best suited for most extensive data sets, high-dimensional spaces, and the most frequently used optimization algorithm [53]. Table 1 lists the model's performance when using different optimization algorithms. As presented in Table 1, the performance of the Adam algorithm and Adadelta algorithm is similar, and the Adam algorithm is slightly better.  
Subsequently, the parameters are initialized to sequence length of 60, the number of neurons (neurons) of 32, and a batch size of 48. The number of iterations is 40. Then each hyperparameter was changed independently, and each test was repeated ten times. The average performance of the model in the training set and the test set is shown in Table 2. It was finally determined that the optimal number of hidden neurons was 64, the batch size was 126, and the dropout was 0.5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimization Algorithms** & **Train Set Performance (RMSE)** & **Test Set Performance (RMSE)** \\ \hline RMSprop & 2.170702 & 2.236931 \\ Adadelta & 2.13235 & 2.190765 \\ Adamax & 2.188046 & 2.227352 \\ Adam & 2.131022 & 2.190727 \\ SGD & 2.253638 & 2.214008 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model’s performance when using different optimization algorithms/dB(A).