# Noise Prediction Using Machine Learning with Measurements Analysis
## 1 Introduction  
Noise pollution is often overlooked in many working environments, which are very often noise-filled [1, 2]. According to the Environmental Protection Agency (EPA), the volume of human speech is approximately 60 dBA. Moreover, people will feel irritable, nervous, unable to concentrate, and will be affected by prolonged exposure to environmental noise at 70 dBA [3]. Long-term exposure to noise at more than 85 dBA will cause chronic hearing damage and can indirectly cause occupational disasters [3]. Likewise, laboratories contain many equipment that generate noise, which distracts researchers and impairs their ability to concentrate. Thus, locating noise sources, predicting future noise levels, and altering environmental factors are important research topics that could be improved to protect against noise, which is important for safe and productive work environments. Many existing prediction models for acoustical properties and traffic noise still have problems with accuracy limitations. For example, the grey model (GM) with Fourier correction gray model (FGM)was proposed to predict the normal incidence sound absorption coefficient and tire/road noise [4]. Based on the analysis of the Federal Highway Administration (FHWA) traffic noise model, a new simplified prediction method was proposed that showed the connection between the traffic noise increments with increases in traffic volume [5]. Another study predicted the total industrial production output value, which will help the design plan of the development city [6]. According to nonlinear time series such as noise data, the gradient lifting technology model is recognized in the prediction of nonlinear time series with a high accuracy rate [6]. Successful examples include predicting real estate sales prices, which includes noise similar to that of time series data [7]. A recent study applied this method for forecasting air quality in Taiwan and also extracted meaningful time and historical features as input to the gradient lifting technology model [8]. Although some literature has shown that gradient lifting technology models work well for predicting certain targets that included a decision tree getting initial values for the fitting function with multiple regression, which treated the many input variables considered in this research. However, the observed data information and output values are calculated error, which uses a loss function. The frequently used loss functions include square-error, absolute-error, and negative binomial log-likelihood functions [8]. Then, gradient lifting technology was applied to find the fitting function where the expected value of loss function is minimized. This procedure was repeated to acquire the optimized fitting function. Unfortunately, their application in noise prediction is very limited. To the best of our knowledge, predicting future noise using the gradient boosting model (GBM) [9] has not been addressed in the existing literature. In fact, the existing noise information belongs to time series data, and their closeness to time is similar to other predicted targets. This motivates our use of the GBM prediction model in a noisy environment, which enables efficient identification of suitable training features in response to different environments and noise conditions, thereby achieving robust and reliable prediction results. Following this, the method proposed in this paper can effectively select the appropriate features as the model input for different characteristics of the noise fields. Moreover, it has good portability, which will be useful for the conversion of many noise sources in the future. The paper of this purpose was to analyze the noise equivalent level (Leq (dBA)) [10; 11; 12; 13; 14] in a work environment that contained the most seriously affected zones. It was evident from long-term monitoring that the highest dBA levels occurred on a certain day every week, that the dBA of certain frequency bands was always higher, and that the dBA levels differed between morning and night.