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

Torija Martinez, AJ; Ruiz, DP

Abstract

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)).

Citation

Torija Martinez, A., & Ruiz, D. (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, 680-693. https://doi.org/10.1016/j.scitotenv.2014.08.060

• Journal Article Type: Article
• Acceptance Date: Aug 19, 2014
• Online Publication Date: Oct 30, 2014
• Publication Date: Feb 1, 2015
• Deposit Date: Dec 3, 2019
• Journal: Science of the Total Environment
• Print ISSN: 0048-9697
• Publisher: Elsevier
• Volume: 505
• Pages: 680-693
• DOI: https://doi.org/10.1016/j.scitotenv.2014.08.060
• Publisher URL: https://doi.org/10.1016/j.scitotenv.2014.08.060
• Related Public URLs: https://www.sciencedirect.com/journal/science-of-the-total-environment
• Additional Information: 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; Grant Number: Agreement Grant No. 246550; Grant Number: COFUND2013-40259; Grant Number: TEC2012-38883-C02-02