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A coupled meteorological classification and uncertainty interval evaluation approach for long-term noise level estimation

Authors
  • Alarcon, Albert
  • Ecotière, David
  • Gauvreau, Benoit
  • LEFEVRE, Hubert
Publication Date
Dec 07, 2020
Identifiers
DOI: 10.48465/fa.2020.0377
OAI: oai:HAL:hal-03233659v1
Source
HAL-Mines ParisTech
Keywords
Language
English
License
Unknown
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Abstract

The estimation of the ?long-term? (typically representative of one year period) noise level descriptor Leq<sub>LT</sub> is a major challenge/issue for the characterization of an environmental acoustic situation, whether it concerns road, rail, industrial or other outdoor noise situations. In this work we study the possibility of estimating the above-mentioned long-term descriptor by combining (i) a classification and variance analysis technique of both observed meteorological and acoustic conditions and (ii) a confidence interval approach to finally estimate the sought descriptor for a longer non-observed period of time. The classification technique is based on a method proposed by the French standard NF S 31-120 concerning the evaluation of the influence of meteorological and ground effects on acoustic levels in an outdoor environment. The long-term noise level is estimated thanks to a confidence interval approach, which width depends on long-term meteorological statistics and on the duration of measurement period. In this context, the theoretical aspects of the coupling of the two techniques are first developed. In a second step, the approach is illustrated through examples using a 2-year database including acoustic and meteorological measurements. In particular, the stability and convergence rate of the interval estimator is studied as a function of the duration of the observed conditions vs. the unobserved conditions over the 2-year reference period. Results show good agreement between the real long-term noise descriptor of the database (Leq<sub>LTₑxp</sub>) and its estimation by means of the proposed technique (Leq<sub>LT_th</sub>). Furthermore, a discussion is proposed concerning the importance of the observation period length and the classification parameters. As a conclusion, a discussion is proposed concerning the adequacy of this technique to address complex outdoor noise situation where such a long-term descriptor estimation is required.

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