Geospatial estimation of noise levels between sparsely distributed sensor nodes using machine learning

• Published in: The Journal of the Acoustical Society of America 2018-09, Vol.144 (3), p.1685-1685
• Main Authors: Blevins, Matthew G., Ochi, Gordon M.
• Format: Article
• Language: English
• ISSN: 0001-4966; 1520-8524
• DOI: 10.1121/1.5067494

Monitoring noise levels over large areas is typically limited by the number of sensor nodes. While previous studies have been able to accurately estimate noise levels using densely distributed sensors in confined spaces, such as along roads in urban areas, estimating noise levels with relatively few sensors spaced over a large area remains a challenging problem. Point sampling of noise levels due to single noise events is also limited by the models used to estimate between sensor locations and their inherent assumptions. To address these limitations we propose nonlinear, adaptive, and robust tools to estimate levels between sensor nodes based on machine learning. Random forests, support vector machines, and Gaussian processes are explored along with conventional geostatistical methods such as ordinary kriging. These methods are trained and evaluated on data from both measurement and simulation of blast noise on military testing and training ranges. The performance of the methods is evaluated using cross validation and root-mean-square-error.