L Martin-Fernandez
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 |
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