Abstract
The noise prediction using machine learning is a special study that has recently received increased attention. This is particularly true in workplaces with noise pollution, which increases noise exposure for general laborers. This study attempts to analyze the noise equivalent level (Leq) at the National Synchrotron Radiation Research Center (NSRRC) facility and establish a machine learning model for noise prediction. This study utilized the gradient boosting model (GBM) as the learning model in which past noise measurement records and many other features are integrated as the proposed model makes a prediction. This study analyzed the time duration and frequency of the collected Leq and also investigated the impact of training data selection. The results presented in this paper indicate that the proposed prediction model works well in almost noise sensors and frequencies. Moreover, the model performed especially well in sensor 8 (125 Hz), which was determined to be a serious noise zone in the past noise measurements. The results also show that the root-mean-square-error (RMSE) of the predicted harmful noise was less than 1 dBA and the coefficient of determination (R2) value was greater than 0.7. That is, the working field showed a favorable noise prediction performance using the proposed method. This positive result shows the ability of the proposed approach in noise prediction, thus providing a notification to the laborer to prevent long-term exposure. In addition, the proposed model accurately predicts noise future pollution, which is essential for laborers in high-noise environments. This would keep employees healthy in avoiding noise harmful positions to prevent people from working in that environment.
Highlights
Noise pollution is often overlooked in many working environments, which are very often noise-filled [1,2]
This paper presented an innovative gradient boosting decision tree (GBDT) model to explore the joint effects of comprehensive factors on the traffic accident indicators [24]
To use the gradient boosting model (GBM) model for future Leq value prediction on the 125 Hz frequency band, time characteristics and historical Leq were used as the input data
Summary
Noise pollution is often overlooked in many working environments, which are very often noise-filled [1,2]. 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. The noise provided the NSRRC and contained than samples. The installed the time period from 08:00 on February 1, 2019 to 23:59 on August 31, 2019. The NSRRC noise detection sensors around the work environment. 2a, it showed the circle installed 12 noise detection sensors around the work environment. 24experimental straight line experimental stations that are function research experiments; the locations of the sensors were divided into the inner circle (1–6). Differential function research experiments; the locations of the sensors were divided into the inner and the outer circle (7–12), shown as green dots
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