A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods

Torija Martinez, AJ ORCID: https://orcid.org/0000-0002-5915-3736 and Ruiz, DP 2015, 'A general procedure to generate models for urban environmental-noise pollution using feature selection and machine learning methods' , Science of the Total Environment, 505 , pp. 680-693.

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The prediction of environmental noise in urban environments requires the solution of a complex and non-linear problem, since there are complex relationships among themultitude of variables involved in the characterization andmodelling of environmental noise and environmental-noisemagnitudes.Moreover, the inclusion of the great spatial heterogeneity characteristic of urban environments seems to be essential in order to achieve an accurate environmental-noise prediction in cities. This problem is addressed in this paper, where a procedure based on feature-selection techniques and machine-learning regression methods is proposed and applied to this environmental problem. Threemachine-learning regression methods, which are considered very robust in solving nonlinear problems, are used to estimate the energy-equivalent sound-pressure level descriptor (LAeq). These three methods are: (i) multilayer perceptron (MLP), (ii) sequential minimal optimisation (SMO), and (iii) Gaussian processes for regression (GPR). In addition, because of the high number of input variables involved in environmental-noise modelling and estimation in urban environments, which make LAeq prediction models quite complex and costly in terms of time and resources for application to real situations, three different techniques are used to approach feature selection or data reduction. The feature-selection techniques used are: (i) correlation-based feature-subset selection (CFS), (ii) wrapper for feature-subset selection (WFS), and the data reduction technique is principal-component analysis (PCA). The subsequent analysis leads to a proposal of different schemes, depending on the needs regarding data collection and accuracy. The use of WFS as the feature-selection technique with the implementation of SMO or GPR as regression algorithm provides the best LAeq estimation (R2 = 0.94 and mean absolute error (MAE) = 1.14–1.16 dB(A)).

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Science of the Total Environment
Publisher: Elsevier
ISSN: 0048-9697
Related URLs:
Funders: University ofMalaga and the European Commission, seventh Framework Programme for R & D of the EU, granted within the People Programme, “Co-funding of Regional, National and International Programmes” (COFUND), “Ministerio de Economía y Competitividad” of Spain, “Ministerio de Economía y Competitividad” of Spain
Depositing User: Dr Antonio J Torija Martinez
Date Deposited: 03 Dec 2019 15:03
Last Modified: 16 Feb 2022 03:28
URI: https://usir.salford.ac.uk/id/eprint/53215

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