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

2015 ◽  
pp. 182-194
Author(s):  
Joon-Hong Seok ◽  
Su-Jin Lee ◽  
Han-Lim Choi
Keyword(s):  
Gaussian Process ◽  
Source Characterization ◽  
Contaminant Source ◽  
Story Building ◽  
Mcmc Approach

scholarly journals Contaminant source characterization in water distribution systems using binary signals

2012 ◽  
Vol 14 (3) ◽  
pp. 585-602 ◽  
Author(s):  
Jitendra Kumar ◽  
E. Downey Brill ◽  
G. Mahinthakumar ◽  
S. Ranji Ranjithan
Keyword(s):  
Distribution System ◽  
Source Identification ◽  
Distribution Systems ◽  
Water Distribution ◽  
Data Availability ◽  
Source Characterization ◽  
Contaminant Source ◽  
Quality Signals ◽  
Wide Range ◽  
True Source

This paper presents a simulation–optimization-based method for identification of contamination source characteristics in a water distribution system using filtered data from threshold-based binary water quality signals. The effects of quality and quantity of the data on the accuracy of the source identification methodology are investigated. This study also addresses the issue of non-uniqueness in contaminant source identification under various data availability conditions. To establish the robustness and applicability of the methodology, numerous scenarios are investigated for a wide range of contamination incidents associated with two different networks. Results indicate that, even though use of lower resolution sensors lead to more non-unique solutions, the true source location is always included among these solutions.

scholarly journals Deep Gaussian Process-Based Bayesian Inference for Contaminant Source Localization

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 49432-49449 ◽  
Author(s):  
Young-Jin Park ◽  
Piyush M. Tagade ◽  
Han-Lim Choi
Keyword(s):  
Bayesian Inference ◽  
Gaussian Process ◽  
Source Localization ◽  
Contaminant Source
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