Emotional artificial neural network (EANN)-based prediction model of maximum A-weighted noise pressure level

IF 1.7 Q2 ACOUSTICS

Noise Mapping Pub Date : 2021-12-04 DOI:10.1515/noise-2022-0001

Sergey V. Kuznetsov, W. Siswanto, F. Sabirova, I. Pustokhina, Lucia Balejčíková, R. Zakieva, M. Nomani, Ferry Fadzlul Rahman, Ismail Husein, Lakshmi Thangavelu

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引用次数: 4

Abstract

Abstract Noise is considered one of the most critical environmental issues because it endangers the health of living organisms. For this reason, up-to-date knowledge seeks to find the causes of noise in various industries and thus prevent it as much as possible. Considering the development of railway lines in underdeveloped countries, identifying and modeling the causes of vibrations and noise of rail transportation is of particular importance. The evaluation of railway performance cannot be imagined without measuring and managing noise. This study tried to model the maximum A-weighted noise pressure level with the information obtained from field measurements by Emotional artificial neural network (EANN) models and compare the results with linear and logarithmic regression models. The results showed the high efficiency of EANN models in noise prediction so that the prediction accuracy of 95.6% was reported. The results also showed that in noise prediction based on the neural network-based model, the independent variables of train speed and distance from the center of the route are essential in predicting.

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基于情感人工神经网络(EANN)的最大a加权噪声压力级预测模型

摘要噪声被认为是最重要的环境问题之一，因为它危害生物体的健康。因此，最新的知识试图找出各个行业噪音的原因，从而尽可能地防止噪音。考虑到欠发达国家铁路线路的发展，识别和建模铁路运输振动和噪声的原因尤为重要。如果不测量和管理噪音，就无法想象对铁路性能的评估。本研究试图利用情绪人工神经网络（EANN）模型从现场测量中获得的信息对最大A加权噪声压力水平进行建模，并将结果与线性和对数回归模型进行比较。结果表明，EANN模型在噪声预测中具有较高的效率，预测准确率达到95.6%。结果还表明，在基于神经网络模型的噪声预测中，列车速度和距路线中心距离的自变量在预测中是必不可少的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Noise Mapping ACOUSTICS-

CiteScore

7.80

自引率

17.90%

发文量

审稿时长

12 weeks

期刊介绍： Ever since its inception, Noise Mapping has been offering fast and comprehensive peer-review, while featuring prominent researchers among its Advisory Board. As a result, the journal is set to acquire a growing reputation as the main publication in the field of noise mapping, thus leading to a significant Impact Factor. The journal aims to promote and disseminate knowledge on noise mapping through the publication of high quality peer-reviewed papers focusing on the following aspects: noise mapping and noise action plans: case studies; models and algorithms for source characterization and outdoor sound propagation: proposals, applications, comparisons, round robin tests; local, national and international policies and good practices for noise mapping, planning, management and control; evaluation of noise mitigation actions; evaluation of environmental noise exposure; actions and communications to increase public awareness of environmental noise issues; outdoor soundscape studies and mapping; classification, evaluation and preservation of quiet areas.