Machine learning-based modeling of noise in warehouse environments

Abstract

The expansion of industrial and logistics facilities increases the impact of environmental noise on residential areas, making it essential to apply accurate and reliable methods for noise assessment and prediction. The aim of this study is to model the dispersion of noise generated during warehouse operations and to evaluate the suitability of machine learning methods for predicting noise levels. Noise calculations were performed using Inter-Model Integration (IMMI) software according to the ISO 9613-2 methodology and the requirements of the Lithuanian hygiene standard HN 33:2011. The spatial distribution of noise was visualized in a GIS (Geographic Information Systems) environment by calculating zonal raster statistical indicators. Predictive modeling was performed using five machine learning algorithms - Random Forest, M5P, Multilayer Perceptron, SMOreg, and Linear Regression - implemented within the WEKA environment. Results indicate that noise generated by warehouse operations near the closest residential areas did not exceed the regulatory limits, with the highest noise levels observed in areas of high traffic flow. The machine learning (ML) algorithms demonstrated very high prediction accuracy - all tested models achieved a correlation coefficient (r) above 0.97. ML analysis revealed particularly high predictive accuracy when using Random Forest (r = 0.9984) and M5P (r = 0.9898) algorithms. These findings confirm that integrating zonal raster statistical indicators with machine learning methods is an effective tool for analyzing industrial noise dispersion and can be successfully applied for practical environmental noise assessment and planning purposes.