Deep Gaussian Process-Based Bayesian Inference for Contaminant Source Localization

TL;DR

This paper introduces a Bayesian method utilizing a deep Gaussian process emulator for efficient and accurate localization of multiple hazardous contaminant sources within a building, integrating sensor data and CFD models.

Contribution

It develops a deep matrix-variate Gaussian process emulator to efficiently approximate CFD models, enabling real-time Bayesian inference for contaminant source localization.

Findings

Accurately localizes multiple contaminant sources in a building.

DMGPE outperforms GP and DGP emulators with fewer hyperparameters.

Demonstrates effectiveness in single-story building scenarios.

Abstract

This paper proposes a Bayesian framework for localization of multiple sources in the event of accidental hazardous contaminant release. The framework assimilates sensor measurements of the contaminant concentration with an integrated multizone computational fluid dynamics (multizone-CFD) based contaminant fate and transport model. To ensure online tractability, the framework uses deep Gaussian process (DGP) based emulator of the multizone-CFD model. To effectively represent the transient response of the multizone-CFD model, the DGP emulator is reformulated using a matrix-variate Gaussian process prior. The resultant deep matrix-variate Gaussian process emulator (DMGPE) is used to define the likelihood of the Bayesian framework, while Markov Chain Monte Carlo approach is used to sample from the posterior distribution. The proposed method is evaluated for single and multiple contaminant sources localization tasks modeled by CONTAM simulator in a single-story building of 30 zones, demonstrating that proposed approach accurately perform inference on locations of contaminant sources. Moreover, the DMGP emulator outperforms both GP and DGP emulator with fewer number of hyperparameters.