根据丹麦中部日德兰地区的噪声测量结果对 Nord2000、RTN-96 和 CNOSSOS-EU 进行性能评估

Acoustics Pub Date : 2023-11-21 DOI:10.3390/acoustics5040062
Jibran Khan, E. Thysell, C. Backalarz, P. Finne, Ole Hertel, S. Jensen
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引用次数: 0

摘要

本文旨在评估北欧和欧洲噪声预测标准 Nord2000、RTN-96 和 CNOSSOS-EU 在预测每日 LAeq24h 和 Lden 水平(dBA)方面的性能,并将其与在丹麦中部日德兰半岛赫尔斯泰德的 E45 高速公路上收集的 76 天测量数据进行比较。此外,文章还研究了利用置信度加权平均(CWA)进行数据融合以提高噪声估计精度的潜在可行性。结果表明,斯皮尔曼相关性(RS)呈高度正相关,反映了观测数据与预测数据之间的高度一致,Nord2000 = 0.85-0.98,CNOSSOS-EU = 0.79-0.92,RTN-96 = 0.86-0.91。模型差异 RMSE = 0.4-3.3 dBA(Nord2000)、1.4 = 2.8 dBA(CNOSSOS)和 1.3-4.2 dBA(RTN-96),主要是由于基本模型参数化和模型输入的不确定性造成的。总体而言,Nord2000 在再现观测噪声水平方面优于 CNOSSOS 和 RTN-96。此外,CNOSSOS 与测量数据吻合良好,在噪声绘图和健康评估方面具有很高的潜力。同样,CWA 被认为是一种有前途的前瞻性数据融合方法,可提高噪声估计的准确性。需要进行更多的研究,以便在更大的地理区域和不同的时间尺度(如每小时、每年)上对模型进行更详细的评估。
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Performance Evaluation of Nord2000, RTN-96 and CNOSSOS-EU against Noise Measurements in Central Jutland, Denmark
This article aims to assess the performance of Nord2000, RTN-96, and CNOSSOS-EU, the Nordic and European noise prediction standards, in predicting daily LAeq24h and Lden levels (dBA), by comparing them with measurements gathered over 76 days from the E45 motorway in Helsted, Central Jutland, Denmark. In addition, the article investigates the potential viability of utilizing Confidence-Weighting Average (CWA) for data fusion to enhance noise estimation accuracy. The results showed highly positive Spearman’s correlations (RS), reflecting strong agreements between observed and predicted data, Nord2000 = 0.85–0.98, CNOSSOS-EU = 0.79–0.92 and RTN-96 = 0.86–0.91. Model differences, RMSE = 0.4–3.3 dBA (Nord2000), 1.4 = 2.8 dBA (CNOSSOS) and 1.3–4.2 dBA (RTN-96), were mainly due to underlying model parametrization and uncertainties in model inputs. Overall, Nord2000 outperformed CNOSSOS and RTN-96 in reproducing observed noise levels. Moreover, CNOSSOS agreed well with the measured data and exhibited a high potential for noise mapping and health assessments. Likewise, the CWA is found to be a promising, forward-looking data fusion approach to improve noise estimates’ accuracy. More research is required to further evaluate the models in greater detail over a larger geographical area and across varied temporal scales (e.g., hourly, yearly).
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