A Bayesian method for model selection in environmental noise prediction

Martin-Fernandez, L, Ruiz, DP, Torija Martinez, AJ ORCID: https://orcid.org/0000-0002-5915-3736 and Miguez, J 2016, 'A Bayesian method for model selection in environmental noise prediction' , Journal of Environmental Informatics, 27 (1) , pp. 31-42.

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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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Journal of Environmental Informatics
Publisher: International Society for Environmental Information Sciences
ISSN: 1726-2135
Related URLs:
Funders: “Ministerio de Economía y Competitividad” of Spain, Ministry of Science and Innovation 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)
Depositing User: Dr Antonio J Torija Martinez
Date Deposited: 03 Dec 2019 10:46
Last Modified: 16 Feb 2022 03:27
URI: https://usir.salford.ac.uk/id/eprint/53207

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