Contaminant Source Identification from Finite Sensor Data: Perron–Frobenius Operator and Bayesian Inference

Application of Bayesian inference methods to inverse modeling for contaminant source identification at Gloucester Landfill, Canada

Probabilistic Contaminant Source Identification in Water Distribution Infrastructure Systems

Large water distribution systems can be highly vulnerable to penetration of contaminant factors caused by different means including deliberate contamination injections. As contaminants quickly spread into a water distribution network, rapid characterization of the pollution source has a high measure of importance for early warning assessment and disaster management. In this paper, a methodology...

Bayesian Nonparametric and Parametric Inference

This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.

Stochastic Event Reconstruction of Atmospheric Contaminant Dispersion Using Bayesian Inference

Environmental sensors have been deployed in various cities for early detection of contaminant releases into the atmosphere. Event reconstruction and improved dispersion modeling capabilities are needed to estimate the extent of contamination, which is required to implement effective strategies in emergency management. To this end, a stochastic event reconstruction capability that can process in...

probabilistic contaminant source identification in water distribution infrastructure systems

large water distribution systems can be highly vulnerable to penetration of contaminant factors caused by different means including deliberate contamination injections. as contaminants quickly spread into a water distribution network, rapid characterization of the pollution source has a high measure of importance for early warning assessment and disaster management. in this paper, a methodology...