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2015 | OriginalPaper | Chapter

A Gaussian Process-Enabled MCMC Approach for Contaminant Source Characterization in a Sensor-Rich Multi-Story Building

Authors : Joon-Hong Seok, Su-Jin Lee, Han-Lim Choi

Published in: Dynamic Data-Driven Environmental Systems Science

Publisher: Springer International Publishing

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Abstract

This paper presents contaminant source localization and characterization in a sensor-rich multi-story building with a large-scale domain. Bayesian framework infers the posterior distribution of source location and characteristics from the sensor network with the model uncertainty and inaccurate prior knowledge. A Markov Chain Monte Carlo method with a Metropolis-Hastings algorithm provides samples extracted from the posterior distribution. A computationally efficient Gaussian process emulator allows Markove Chain Monte Carlo sampling to use a physics-based model with tractable computational cost and time. The posterior distribution obtained by the proposed method through hypothetical contaminant release in a four-story building with total 156 subzones and sensors approaches true values of parameters of interest closely and shows the efficacy for parameter inference in a large-scale domain.

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Metadata
Title
A Gaussian Process-Enabled MCMC Approach for Contaminant Source Characterization in a Sensor-Rich Multi-Story Building
Authors
Joon-Hong Seok
Su-Jin Lee
Han-Lim Choi
Copyright Year
2015
Book
Dynamic Data-Driven Environmental Systems Science

Print ISBN: 978-3-319-25137-0

Electronic ISBN: 978-3-319-25138-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-25138-7_17

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