Modeling outdoor acoustic environments is a challenging problem because outdoor acoustic environments are the combination of diverse sources and propagation effects, including barriers to propagation such as buildings or vegetation. Outdoor acoustic environments are most commonly modeled on small geographic scales (e.g., within a single city). Extending modeling efforts to continental scales is particularly challenging due to an increase in the variety of geographic environments. Furthermore, acoustic data on which to train and validate models are expensive to collect and therefore relatively limited. It is unclear how models trained on this limited acoustic data will perform across continental-scales, which likely contain unique geographic regions which are not represented in the training data. In this dissertation, we consider the problem of continental-scale outdoor environmental sound level modeling using the contiguous United States for our area of study. We use supervised machine learning methods to produce models of various acoustic metrics and unsupervised learning methods to study the natural structures in geospatial data. We present a validation study of two continental-scale models which demonstrates that there is a need for better uncertainty quantification and tools to guide data collection. Using ensemble models, we investigate methods for quantifying uncertainty in continental-scale models. We also study methods of improving model accuracy, including dimensionality reduction, and explore the feasibility of predicting hourly spectral levels.

Modeling outdoor acoustic environments is a challenging problem because outdoor acoustic environments are the combination of diverse sources and propagation effects, including barriers to propagation such as buildings or vegetation. Outdoor acoustic environments are most commonly modeled on small geographic scales (e.g., within a single city). Extending modeling efforts to continental scales is particularly challenging due to an increase in the variety of geographic environments. Furthermore, acoustic data on which to train and validate models are expensive to collect and therefore relatively limited. It is unclear how models trained on this limited acoustic data will perform across continental-scales, which likely contain unique geographic regions which are not represented in the training data. In this dissertation, we consider the problem of continental-scale outdoor environmental sound level modeling using the contiguous United States for our area of study. We use supervised machine learning methods to produce models of various acoustic metrics and unsupervised learning methods to study the natural structures in geospatial data. We present a validation study of two continental-scale models which demonstrates that there is a need for better uncertainty quantification and tools to guide data collection. Using ensemble models, we investigate methods for quantifying uncertainty in continental-scale models. We also study methods of improving model accuracy, including dimensionality reduction, and explore the feasibility of predicting hourly spectral levels.