The impact of urban vegetation cover in noise pollution reduction using artificial neural network

Document Type : Independent Research Articles

Authors

1 Associate Professor, Department of Assessment and Environment Risks, Research Center of Environment and Sustainable Development, College of Environment, Tehran, Iran

2 MSc. Student of Environment Design, Department of Environmental Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

Noise pollution is an important factor in determining the quality of life in cities and affects social welfare. In this regard, vegetation and green space have an effective role in controlling and reducing urban pollution. Evaluation and modeling of the effective structure and composition of vegetation to control noise pollution in cities reveals the limitation in studies so it has been chosen as the main goal of this research. Noise intensity sampling was performed at 100 stations in parks and passages in urban districts 1 and 2 of Tehran. In order to model the amount of noise reduction (Leq) in the vegetation acoustic wall, artificial neural network modeling was performed using 9 vegetation variables. According to the results, the model with 9-24-2 structure (9 input variables, 24 neurons in the hidden layer and one output variable) with respect to the highest value of coefficient of determination in the three categories of training data, validation and test equal to 0.98, 0.92 0 and 0.9 reveals the best structure optimization performance. Based on the results of model sensitivity analysis, wall width, the mean height of trees, and the mean diameter of canopy with the coefficients of 0.72, 0.44 and 0.15, respectively, were the most effective in reducing the noise intensity in the plant acoustic walls, respectively. The model presented in this study is known as a decision support system in design of vegetation acoustic walls in cities and enables the prediction of the efficiency of these walls with respect to structural variables.

Keywords

Urban Structure and Function Studies
Volume 8, Issue 27
August 2021
Pages 161-179

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History

  • Receive Date: 01 June 2020
  • Revise Date: 12 September 2020
  • Accept Date: 22 August 2021

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