Pub Date : 2023-11-21DOI: 10.3390/acoustics5040062
Jibran Khan, E. Thysell, C. Backalarz, P. Finne, Ole Hertel, S. Jensen
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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Pub Date : 2023-11-14DOI: 10.3390/acoustics5040061
Md Iltaf Zafar, S. Bharadwaj, R. Dubey, S. Tiwary, Susham Biswas
The accurate prediction of noise levels at outdoor locations requires detailed data of the noise sources and terrain parameters and an efficient model for prediction. However, the possibility of predicting noise with reasonable accuracy using less input data is a challenge and needs to be studied scientifically. The qualities of the noise data, terrain parameters, and prediction model can impact the accuracy of the prediction significantly. This study primarily focuses on the dependency of noise data for efficient noise prediction and mapping. This research article proposes a detailed methodology to predict and map the noise and exposure levels in Ratapur, Uttar Pradesh, India, with various granularities of noise data inputs. The noise levels were measured at various places and at different times of the day at 10 min intervals. Different data input proportions and qualities were used for noise prediction, namely, (1) a large data-based method, (2) a small data-based method, (3) a source point average data-based method, (4) a Google navigation data-based method, and (5) accurate modelling using an ANN-based method, integrating accurate noise data with a sophisticated modelling algorithm for noise prediction. The analysis of the variation between the predicted and measured noise levels was conducted for all five of the methods using the ANOVA technique. Various methods based on less noise data methods predicted the noise levels with accuracies within the ±4–10 dB(A) range, while the ANN-based technique predicted it with an accuracy of ±0.5–2.5 dB(A). Interestingly, the estimation of the noise exposure levels (>85 dB(A)) and the identification of hazard zones around the studied road intersection could also be performed efficiently even when using the data-deficient models. This paper also showcased the possibility of predicting an accurate 3D map for an area by extracting vehicles and terrain features from satellite images without any direct recording of noise data. This paper thus demonstrated approaches to reduce the noise data dependency for noise prediction and mapping and to enable accurate noise-hazard zonation mapping.
要准确预测室外地点的噪声水平，需要噪声源和地形参数的详细数据以及有效的预测模型。然而，如何利用较少的输入数据合理准确地预测噪声是一项挑战，需要进行科学研究。噪声数据、地形参数和预测模型的质量会对预测的准确性产生重大影响。本研究主要关注噪声数据对高效噪声预测和绘图的依赖性。本研究文章提出了一种详细的方法，利用不同粒度的噪声数据输入，预测和绘制印度北方邦拉塔普尔的噪声和暴露水平。在不同地点和一天中的不同时间，以 10 分钟为间隔测量噪声水平。噪声预测采用了不同的数据输入比例和质量，即：(1) 基于大数据的方法；(2) 基于小数据的方法；(3) 基于源点平均数据的方法；(4) 基于谷歌导航数据的方法；(5) 基于 ANN 的精确建模方法，将精确的噪声数据与复杂的噪声预测建模算法相结合。采用方差分析技术对所有五种方法的噪声预测值和测量值之间的变化进行了分析。基于较少噪声数据的各种方法对噪声水平的预测精度在 ±4-10 dB(A)范围内，而基于 ANN 的技术对噪声水平的预测精度为 ±0.5-2.5 dB(A)。有趣的是，即使使用数据不足的模型，也能有效地估算噪声暴露水平（>85 dB(A)），并识别所研究道路交叉口周围的危险区域。本文还展示了在没有直接记录噪声数据的情况下，通过从卫星图像中提取车辆和地形特征来预测一个区域的精确三维地图的可能性。因此，本文展示了减少噪声预测和绘图对噪声数据依赖的方法，以及实现精确噪声危害分区绘图的方法。
{"title":"Reducing Data Requirements for Simple and Effective Noise Mapping: A Case Study of Noise Mapping Using Computational Methods and GIS for the Raebareli City Intersection","authors":"Md Iltaf Zafar, S. Bharadwaj, R. Dubey, S. Tiwary, Susham Biswas","doi":"10.3390/acoustics5040061","DOIUrl":"https://doi.org/10.3390/acoustics5040061","url":null,"abstract":"The accurate prediction of noise levels at outdoor locations requires detailed data of the noise sources and terrain parameters and an efficient model for prediction. However, the possibility of predicting noise with reasonable accuracy using less input data is a challenge and needs to be studied scientifically. The qualities of the noise data, terrain parameters, and prediction model can impact the accuracy of the prediction significantly. This study primarily focuses on the dependency of noise data for efficient noise prediction and mapping. This research article proposes a detailed methodology to predict and map the noise and exposure levels in Ratapur, Uttar Pradesh, India, with various granularities of noise data inputs. The noise levels were measured at various places and at different times of the day at 10 min intervals. Different data input proportions and qualities were used for noise prediction, namely, (1) a large data-based method, (2) a small data-based method, (3) a source point average data-based method, (4) a Google navigation data-based method, and (5) accurate modelling using an ANN-based method, integrating accurate noise data with a sophisticated modelling algorithm for noise prediction. The analysis of the variation between the predicted and measured noise levels was conducted for all five of the methods using the ANOVA technique. Various methods based on less noise data methods predicted the noise levels with accuracies within the ±4–10 dB(A) range, while the ANN-based technique predicted it with an accuracy of ±0.5–2.5 dB(A). Interestingly, the estimation of the noise exposure levels (>85 dB(A)) and the identification of hazard zones around the studied road intersection could also be performed efficiently even when using the data-deficient models. This paper also showcased the possibility of predicting an accurate 3D map for an area by extracting vehicles and terrain features from satellite images without any direct recording of noise data. This paper thus demonstrated approaches to reduce the noise data dependency for noise prediction and mapping and to enable accurate noise-hazard zonation mapping.","PeriodicalId":502373,"journal":{"name":"Acoustics","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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