Data-driven prediction of peak sound levels at long range using sparse, ground-level meteorological measurements and a random forest

Outdoor sound propagation is highly dependent upon meteorological conditions. While this, of course, is a trivial statement, predicting sound levels based on meteorology is not. This is especially true for signals that propagate many kilometers, as is the case for those generated by high-energy impulsive sources such as explosions and heavy weaponry; waves have ample opportunity for refraction by and scattering from local atmospheric features along the entire propagation path. The range of received blast levels at distances greater than 2 km can span nearly 50 dB, depending on weather conditions. Using a statistical learning method known as a Random Forest, we demonstrate the prediction of levels from simple meteorological measurements in the face of this extreme variability. With simple, spatially sparse meteorological data, the model can predict levels to within 3 dB at 2 km and 5 dB at 15 km. The results presented here suggest that as more data are acquired through continuous noise monitoring programs,...