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Machine Learning for Noise Prediction by Antonio Sánchez is a document available to read on EtoBox.

This study focuses on predicting noise pollution in workplaces using a machine learning model, specifically the gradient boosting model (GBM), to analyze noise equivalent levels (Leq) at the National Synchrotron Radiation Research Center. The model demonstrated strong predictive performance with an RMSE of less than 1 dBA and an R2 value greater than 0.7, indicating its effectiveness in forecasting harmful noise levels. The findings aim to enhance worker safety by providing timely notifications to prevent l

Author

Antonio Sánchez

Language

EN