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...

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Bibliographic Details
Published in:Acoustics (Basel, Switzerland) Switzerland), 2023-12, Vol.5 (4), p.1099-1122
Main Authors: Khan, Jibran, Thysell, Erik, Backalarz, Claus, Finne, Per, Hertel, Ole, Jensen, Steen Solvang
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
CNOSSOS-EU
Data integration
Data processing
Environmental protection
machine learning
measurements
model validation
Noise levels
Noise prediction
Nord2000
Parameterization
Performance evaluation
Roads & highways
RTN-96
Signal processing
Simulation
Traffic
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Summary: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).
ISSN:2624-599X
2624-599X
DOI:10.3390/acoustics5040062