Explainable machine learning models for outdoor exceedance level prediction based on geospatial variables

Sound level modeling has emerged as an essential tool for predicting acoustic environments. We present the development and analysis of models using a dataset previously applied for sound exceedance level modeling in the contiguous United States. This dataset comprises acoustic exceedance levels measured in diverse locations including National Park Service sites and urban environments. We applied advanced python libraries to train Random Forest regression models to predict exceedance levels from 99 geospatial variables. In total, 3 general and 5 ancillary fully data-driven models (not modeling actual physics of sound propagation) were developed, and the particular performance and limitations of each model is discussed. Results show promising predictive power, with R2 between 0.54 and 0.91 and root mean squared error between 1.77 and 5.97 dB, where models incorporating more urban information performed better. These results highlight the strength of the models, with performance variability primarily attributed to the limited coverage of diverse natural and urban environments in the current dataset. Results are accessible via an interactive online dashboard, allowing users without machine learning expertise to analyze different aspects of the models. This platform supports broader accessibility, encouraging a wider audience to engage with outdoor sound level modeling and its applications.