A Bayesian method for model selection in environmental noise prediction

Martin-Fernandez, L; Ruiz, DP; Torija Martinez, AJ; Miguez, J

Abstract

Environmental noise prediction and modeling are key factors for addressing a proper planning and management of
urban sound environments. In this paper we propose a maximum a posteriori (MAP) method to compare nonlinear state-space models
that describe the problem of predicting environmental sound levels. The numerical implementation of this method is based on particle
filtering and we use a Markov chain Monte Carlo technique to improve the resampling step. In order to demonstrate the validity of the
proposed approach for this particular problem, we have conducted a set of experiments where two prediction models are quantitatively
compared using real noise measurement data collected in different urban areas.

Citation

Martin-Fernandez, L., Ruiz, D., Torija Martinez, A., & Miguez, J. (2016). A Bayesian method for model selection in environmental noise prediction. Journal of Environmental Informatics, 27(1), 31-42. https://doi.org/10.3808/jei.201500295

• Journal Article Type: Article
• Acceptance Date: Jan 20, 2015
• Online Publication Date: Mar 11, 2016
• Publication Date: Mar 11, 2016
• Deposit Date: Dec 3, 2019
• Journal: Journal of Environmental Informatics
• Print ISSN: 1726-2135
• Publisher: International Society for Environmental Information Sciences
• Volume: 27
• Issue: 1
• Pages: 31-42
• DOI: https://doi.org/10.3808/jei.201500295
• Publisher URL: http://dx.doi.org/10.3808/jei.201500295
• Related Public URLs: http://www.jeionline.org/index.php?journal=mys&page=index
• Additional Information: Funders : “Ministerio de Economía y Competitividad” of Spain;Ministry of Science and Innovation of Spain;University of Malaga and the European Commission under the Agreement Grant no. 246550 of the seventh Framework Programme for R & D of the EU, granted within the People Programme, Co-funding of Regional, National and International Programmes (COFUND); Grant Number: TEC 2012-38883-C02-02; Grant Number: Consolider- Ingenio 2010 CSD2008-00010 COMONSENS; Grant Number: COMPREHENSION TEC2012-38883-C02-01; Grant Number: Agreement Grant no. 246550