Temporal and spatial evaluation of environmental noise in urban area: a case study in Iran

Ramazani, Mohammad Ebrahim; Mosaferi, Mohammad; Rasoulzadeh, Yahya; Pourakbar, Mojtaba; Jafarabadi, Mohammad Asghari; Amini, Heresh

doi:10.1007/s13762-017-1462-4

Cited by 8 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GIS, due to its user-friendly interface and built-in applications, has been used extensively to generate noise maps, , process traffic-related data, and develop noise models . However, GIS approaches do not provide the necessary flexibility to incorporate acoustic or data features with complex interactions.…”

Section: Introductionmentioning

confidence: 99%

Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning

Yin

Fallah-Shorshani

McConnell

et al. 2020

Environ. Sci. Technol.

View full text Add to dashboard Cite

Environmental noise has been associated with a variety of health endpoints including cardiovascular disease, sleep disturbance, depression, and psychosocial stress. Most population noise exposure comes from vehicular traffic, which produces fine-scale spatial variability that is difficult to characterize using traditional fixed-site measurement techniques. To address this challenge, we collected A-weighted, equivalent noise (LAeq in decibels, dB) data on hour-long foot journeys around 16 locations throughout Long Beach, California and trained four machine learning models, linear regression, random forest, extreme gradient boosting, and a neural network, to predict noise with 20 m resolution. Input variables to the models included traffic metrics, road network features, meteorological conditions, and land use type. Among all machine learning models, extreme gradient boosting had the best results in validation tests (leave-one-route-out R 2 = 0.71, root mean square error (RMSE) of 4.54 dB; 5-fold R 2 = 0.96, RMSE of 1.8 dB). Local traffic volume was the most important predictor of noise; road features, land use, and meteorology including humidity, temperature, and wind speed also contributed. We show that a novel, on-foot mobile noise measurement method coupled with machine learning approaches enables highly accurate prediction of small-scale spatial patterns in traffic-related noise over a mixed-use urban area.

show abstract

Section: Introductionmentioning

confidence: 99%