Related papers: A Bayesian hierarchical model for methane emission…
Methane (CH4) is a potent greenhouse gas, and its detection and quantification are crucial for mitigating the greenhouse effect. This study presents a comparative analysis of methane emissions observed using near-simultaneous observations…
Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the…
The problem of locating an odor source in turbulent flows is central to key applications such as environmental monitoring and disaster response. We address this challenge by designing an algorithm based on Bayesian inference, which uses…
Early detection of leaks in gas transmission systems is crucial for ensuring uninterrupted gas supply, enhancing operational efficiency, and minimizing environmental and economic risks. This study aims to develop an analytical method for…
In response to global concerns regarding air quality and the environmental impact of greenhouse gas emissions, detecting and quantifying sources of emissions has become critical. To understand this impact and target mitigations effectively,…
In a wireless sensor network, multilevel quantization is necessary in order to find a compromise between the smallest possible power consumption of the sensors and the detection performance at the fusion center (FC). The general methodology…
The article addresses the problem of detecting presence and location of a small low emission source inside of an object, when the background noise dominates. This problem arises, for instance, in some homeland security applications. The…
We propose a methodology for modelling methane intensities of Oil and Gas upstream activities for different production profiles with diverse combinations of region of operation and production volumes associated. This methodology leverages…
We present a Bayesian approach for the Contamination Source Detection problem in Water Distribution Networks. Given an observation of contaminants in one or more nodes in the network, we try to give probable explanation for it assuming that…
This work presents a procedure that can quickly identify and isolate methane emission sources leading to expedient remediation. Minimizing the time required to identify a leak and the subsequent time to dispatch repair crews can…
This paper presents a new interaction point process that integrates geological knowledge for the purpose of automatic sources detection of multiple sources in groundwaters from hydrochemical data. The observations are considered as spatial…
In this paper, a multipurpose Bayesian-based method for data analysis, causal inference and prediction in the sphere of oil and gas reservoir development is considered. This allows analysing parameters of a reservoir, discovery dependencies…
This paper investigates the sparse optimal allocation of sensors for detecting sparse leaking emission sources. Because of the non-negativity of emission rates, uncertainty associated with parameters in the forward model, and sparsity of…
Considerable financial resources are allocated for measuring ambient air pollution in the United States, yet the locations for these monitoring sites may not be optimized to capture the full extent of current pollution variability. Prior…
This paper proposes an information theory approach to estimate the number of changepoints and their locations in a climatic time series. A model is introduced that has an unknown number of changepoints and allows for series…
In many real-world scenarios, such as gas leak detection or environmental pollutant tracking, solving the Inverse Source Localization and Characterization problem involves navigating complex, dynamic fields with sparse and noisy…
Wastewater monitoring is an effective approach for the early detection of viral and bacterial disease outbreaks. It has recently been used to identify the presence of individuals infected with COVID-19. To monitor large communities and…
Air sensor networks provide hyperlocal, high temporal resolution data on multiple pollutants that can support credible identification of common pollution sources. Source apportionment using least squares-based non-negative matrix…
In this paper, we propose a latent-variable generative model called mixture of dynamical variational autoencoders (MixDVAE) to model the dynamics of a system composed of multiple moving sources. A DVAE model is pre-trained on a…
Previous likelihood-based linear modeling of nutritional data has been limited by the availability of software that allows flexible error structures in the data. We demonstrate the use of a Bayesian modeling approach to the analysis of such…