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Integrating random forests and propagation models for high-resolution noise mapping
Environmental Research ( IF 5.715 ) Pub Date : 2021-02-22 , DOI: 10.1016/j.envres.2021.110905
Ying Liu; Tor Oiamo; Daniel Rainham; Hong Chen; Marianne Hatzopoulou; Jeffrey R. Brook; Hugh Davies; Sophie Goudreau; Audrey Smargiassi

The adverse effects of long-term exposure to environmental noise on human health are of increasing concern. Noise mapping methods such as spatial interpolation and land use regression cannot capture complex relationships between environmental conditions and noise propagation or attenuation in a three-dimension (3D) built environment. In this study, we developed a hybrid approach by combining a traffic propagation model and random forests (RF) machine learning algorithm to map the total environment noise levels for daily average, daytime, nighttime, and day-evening-nighttime at 30 m × 30 m resolution for the island of Montreal, Canada. The propagation model was used to predict traffic noise surfaces using road traffic flow, 3D building information, and a digital elevation model. The traffic noise estimates were compared with ground-based sound-level measurements at 87 points to extract residuals between total environmental noise and traffic noise. Residuals at these points were fit to RF models with multiple environmental and geographic predictor variables (e.g., vegetation index, population density, brightness of nighttime lights, land use types, and distances to noise contour around the airport, bus stops, and road intersections). Using the sound-level measurements as baseline data, the prediction errors, i.e., mean error, mean absolute error, and root mean squared error of daily average noise levels estimated by our hybrid approach was -0.03 dB(A), 2.67 dB(A), and 3.36 dB(A). Combining deterministic and stochastic models can provide accurate total environmental noise estimates for large geographic areas where sound-level measurements are available.



中文翻译:

集成随机森林和传播模型以实现高分辨率噪声映射

长期暴露于环境噪声对人体健康的不利影响正日益引起人们的关注。诸如空间插值和土地利用回归之类的噪声映射方法无法捕获环境条件与噪声在三维(3D)构建环境中的传播或衰减之间的复杂关系。在这项研究中,我们通过结合交通传播模型和随机森林(RF)机器学习算法来开发一种混合方法,以绘制30 m ×30的日平均,白天,夜间和白天晚上的总环境噪声水平加拿大蒙特利尔岛的决议。传播模型用于通过道路交通流量,3D建筑信息和数字高程模型来预测交通噪声表面。将交通噪声估计值与基于地面的声级测量值(在87个点处)进行比较,以提取总环境噪声和交通噪声之间的残差。这些点的残差适合具有多个环境和地理预测变量(例如,植被指数,人口密度,夜间照明灯的亮度,土地使用类型以及到机场,公交车站和路口的噪声轮廓的距离)的RF模型。使用声级测量作为基线数据,预测误差,由我们的混合方法估算的每日平均噪声水平的平均误差,平均绝对误差和均方根误差为-0.03 dB(A),2.67 dB(A)和3.36 dB(A)。确定性模型和随机模型相结合可以为可进行声级测量的较大地理区域提供准确的总环境噪声估计。

更新日期:2021-02-22
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