scholarly journals Peat Carbon Vulnerability to Projected Climate Warming in the Hudson Bay Lowlands, Canada: A Decision Support Tool for Land Use Planning in Peatland Dominated Landscapes

2021 ◽  
Vol 9 ◽  
Author(s):  
James W. McLaughlin ◽  
Maara S. Packalen
Keyword(s):  
Land Cover ◽  
Carbon Storage ◽  
Carbon Sink ◽  
Bayesian Belief Network ◽  
Bayesian Belief Networks ◽  
Sink Strength ◽  
Hudson Bay ◽  
Climate Scenarios ◽  
Belief Network ◽  
Hudson Bay Lowlands

Peatlands help regulate climate by sequestering (net removal) carbon from the atmosphere and storing it in plants and soils. However, as mean annual air temperature (MAAT) increases, peat carbon stocks may decrease. We conducted an in-depth synthesis of current knowledge about ecosystem controls on peatland carbon storage and fluxes to constrain the most influential parameters in probabilistic modelling of peat carbon sinks, such as Bayesian belief networks. Evaluated parameters included climate, carbon flux and mass, land cover, landscape position (defined here as elevation), fire records, and current and future climate scenarios for a 74,300 km2 landscape in the Hudson Bay Lowlands, Canada. The Bayesian belief network was constructed with four tiers: 1) exposure, expressed as MAAT, and the state variables of elevation and land cover; 2) sensitivity, expressed as ecosystem conditions relevant to peat carbon mass and its quality for decomposition, peat wetness, and fire; 3) carbon dioxide and methane fluxes and peat combustion; and 4) vulnerability of peat carbon sink strength under warmer MAAT. Simulations were conducted using current (−3.0 to 0.0°C), moderately warmer (0.1–4.0°C), and severely warmer (4.1–9.0°C) climate scenarios. Results from the severely warmer climate scenario projected an overall drying of peat, with approximately 20% reduction in the strong sink categories of net ecosystem exchange and peat carbon sink strength for the severely and, to a lesser degree, the moderately warmer climate scenarios relative to current MAAT. In the warmest temperature simulation, probability of methane emission decreased slightly and the probability of the strong peat carbon sink strength was 27% lower due to peat combustion. Our Bayesian belief network can assist land planners in decision-making for peatland-dominated landscapes, such as identifying high carbon storage areas and those projected to be at greatest risk of carbon loss due to climate change. Such areas may be designated, for example, as protected or reduced management intensity. The Bayesian belief network presented here is built on an in-depth knowledge synthesis to construct conditional probability tables, so is expected to apply to other peatland-dense jurisdictions by changing only elevation, peatland types, and MAAT.

Bayesian Belief Networks for Data Cleaning

2011 ◽  
pp. 204-219
Author(s):  
Enrico Fagiuoli ◽  
Sara Omerino ◽  
Fabio Stella
Keyword(s):  
Data Quality ◽  
Missing Values ◽  
Data Cleaning ◽  
Joint Probability ◽  
Bayesian Belief Network ◽  
Bayesian Belief Networks ◽  
Computational Techniques ◽  
Belief Network ◽  
General Statistical ◽  
Joint Probability Distributions

The importance of data cleaning and data quality is becoming increasingly clear as evidenced by the surge in software, tools, consulting companies and seminars addressing data quality issues. In this contribution the authors present and describe how Bayesian computational techniques can be exploited for data cleaning purposes to the extent of reducing the time to clean and understand the data. The proposed approach relies on the computational device named Bayesian belief network, which is a general statistical model that allows the efficient description and treatment of joint probability distributions. This work describes the conceptual framework that maps the Bayesian belief network computational device to some of the most difficult tasks in data cleaning, namely imputing missing values, completing truncated datasets and outliers detection. The proposed framework is described and supported by a set of numerical experiments performed by exploiting the Bayesian belief network programming suite named HUGIN.

Algorithms for Bayesian Belief-Network Precomputation

1991 ◽  
Vol 30 (02) ◽  
pp. 81-89 ◽  
Author(s):  
E. H. Herskovits ◽  
G. F. Cooper
Keyword(s):  
Computational Complexity ◽  
Expert Systems ◽  
Expected Utility ◽  
Bayesian Belief Network ◽  
Bayesian Belief Networks ◽  
Future Research ◽  
Belief Networks ◽  
Major Drawback ◽  
Belief Network ◽  
General Method

AbstractBayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference.We first present the necessary background, and then present algorithms for producing caches based on metrics of expected probability and expected utility. We show how these algorithms can be applied to a moderately complex belief network, and present directions for future research.

Overview of Bayesian Belief Network

2009 ◽  
pp. 182-194
Author(s):  
Ben Kei Daniel
Keyword(s):  
Prior Knowledge ◽  
Bayesian Belief Network ◽  
Bayesian Belief Networks ◽  
Basic Knowledge ◽  
Bayesian Probability ◽  
Belief Network ◽  
Wide Range ◽  
Technical Details ◽  
Extraneous Data ◽  
Subjective Belief

Statistical and probability inferences are basically dependent on two major methods of reasoning, conventional (frequentist) and Bayesian probability. Frequentists’ methods are mainly based on numerous events, where Bayesian probability applies prior knowledge and subjective belief. Frequentist models of probability do not permit the introduction of prior knowledge into the calculations. This is traditionally to maintain the rigour of a scientific method and as way to prevent the introduction of extraneous data that might skew the experimental results. However, there are times when the use of prior knowledge would be a useful contribution to evaluation a situation. The Bayesian approach was proposed to help us reason in situation where prior knowledge is need, and especially under highly uncertain circumstances. This Chapter provides an overview of the main principles underlying the Bayesian method and Bayesian belief networks. The ultimate goal is to provide the reader with the basic knowledge necessary for understanding the Bayesian Belief Network approach to building computational model. The Chapter does not go into more technical details of probability theory and Bayesian statistics. But to make it more accessible to a wide range of readers, some technical details are simplified.

scholarly journals Interactions between nitrogen deposition, land cover conversion, and climate change determine the contemporary carbon balance of Europe

2010 ◽  
Vol 7 (9) ◽  
pp. 2749-2764 ◽  
Author(s):  
G. Churkina ◽  
S. Zaehle ◽  
J. Hughes ◽  
N. Viovy ◽  
Y. Chen ◽  
...  
Keyword(s):  
Land Cover ◽  
Nitrogen Deposition ◽  
Carbon Storage ◽  
Carbon Balance ◽  
Agricultural Land ◽  
Environmental Changes ◽  
Driving Forces ◽  
Carbon Sink ◽  
Nitrogen Dynamics ◽  
Continuous Increase

Abstract. European ecosystems are thought to take up large amounts of carbon, but neither the rate nor the contributions of the underlying processes are well known. In the second half of the 20th century, carbon dioxide concentrations have risen by more that 100 ppm, atmospheric nitrogen deposition has more than doubled, and European mean temperatures were increasing by 0.02 °C yr−1. The extents of forest and grasslands have increased with the respective rates of 5800 km2 yr−1 and 1100 km2 yr−1 as agricultural land has been abandoned at a rate of 7000 km2 yr−1. In this study, we analyze the responses of European land ecosystems to the aforementioned environmental changes using results from four process-based ecosystem models: BIOME-BGC, JULES, ORCHIDEE, and O-CN. The models suggest that European ecosystems sequester carbon at a rate of 56 TgC yr−1 (mean of four models for 1951–2000) with strong interannual variability (±88 TgC yr−1, average across models) and substantial inter-model uncertainty (±39 TgC yr−1). Decadal budgets suggest that there has been a continuous increase in the mean net carbon storage of ecosystems from 85 TgC yr−1 in 1980s to 108 TgC yr−1 in 1990s, and to 114 TgC yr−1 in 2000–2007. The physiological effect of rising CO2 in combination with nitrogen deposition and forest re-growth have been identified as the important explanatory factors for this net carbon storage. Changes in the growth of woody vegetation are suggested as an important contributor to the European carbon sink. Simulated ecosystem responses were more consistent for the two models accounting for terrestrial carbon-nitrogen dynamics than for the two models which only accounted for carbon cycling and the effects of land cover change. Studies of the interactions of carbon-nitrogen dynamics with land use changes are needed to further improve the quantitative understanding of the driving forces of the European land carbon balance.

Construction of Bayesian Models

2009 ◽  
pp. 195-206
Author(s):  
Ben Kei Daniel
Keyword(s):  
Quantitative Data ◽  
Knowledge Engineering ◽  
Bayesian Models ◽  
Network Models ◽  
Bayesian Belief Network ◽  
Bayesian Belief Networks ◽  
Basic Principles ◽  
Belief Network ◽  
Building Models ◽  
Engineering Effort

Bayesian Belief Networks (BBNs) are increasingly used for understanding different problems in many domains. Though BBN techniques are elegant ways of capturing uncertainties, knowledge engineering effort required to create and initialize a network has prevented many researchers from using them. Even though the structure of the network and its conditional and initial probabilities could be learned from data, data is not always available and/or too costly to obtain. Furthermore, current algorithms used to learn relationships among variables, initial and conditional probabilities from data are often complex and cumbersome to employ. A qualitative Bayesian network approach was introduced to address some of the difficulties in building models that mainly depend on quantitative data. Building BBN models from quantitative data presupposes that relationships among variables or concepts of interests are known and can be correlated, causally related or they can relate to each other independently. The interdependency or relationships among the variables enable more reliable inferences which in turn help in making informed decisions about results of the model.This Chapter presents qualitative techniques and algorithms for creating Bayesian belief network models. It simplifies the construction of Bayesian models in few steps. The goal of the Chapter is to introduce the reader to the basic principles underlying the constructions of Bayesian Belief Network.

scholarly journals Interactions between nitrogen deposition, land cover conversion, and climate change determine the contemporary carbon balance of Europe

2010 ◽  
Vol 7 (2) ◽  
pp. 2227-2265 ◽  
Author(s):  
G. Churkina ◽  
S. Zaehle ◽  
J. Hughes ◽  
N. Viovy ◽  
Y. Chen ◽  
...  
Keyword(s):  
Land Cover ◽  
Nitrogen Deposition ◽  
Carbon Storage ◽  
Carbon Balance ◽  
Agricultural Land ◽  
Environmental Changes ◽  
Driving Forces ◽  
Carbon Sink ◽  
Terrestrial Ecosystems ◽  
Nitrogen Dynamics

Abstract. European ecosystems are thought to uptake significant amounts of carbon, but neither the rate nor the contributions of the underlying processes are well known. In the second half of the 20th century, carbon dioxide concentrations have risen by more than 100 ppm, atmospheric nitrogen deposition has more than doubled, and European mean temperatures were increasing by 0.02 °C per year. The extents of forest and grasslands have increase with the respective rates of 5800 km2 yr-1 and 1100 km2 yr-1 as agricultural land has been abandoned at a rate of 7000 km2 yr-1. In this study, we analyze the responses of European land ecosystems to the aforementioned environmental changes using results from four process-based ecosystem models: BIOME-BGC, JULES, ORCHIDEE, and O-CN. All four models suggest that European terrestrial ecosystems sequester carbon at a rate of 100 TgC yr-1 (1980–2007 mean) with strong interannual variability (± 85 TgC yr-1) and a substantial inter-model uncertainty (± 45 TgC yr-1). Decadal budgets suggest that there has been a slight increase in terrestrial net carbon storage from 85 TgC yr-1 in 1980–1989 to 114 TgC yr-1 in 2000–2007. The physiological effect of rising CO2 in combination with nitrogen deposition and forest re-growth have been identified as the important explanatory factors for this net carbon storage. Changes in the growth of woody vegetation are an important contributor to the European carbon sink. Simulated ecosystem responses were more consistent for the two models accounting for terrestrial carbon-nitrogen dynamics than for the two models which only accounted for carbon cycling and the effects of land cover change. Studies of the interactions of carbon-nitrogen dynamics with land use changes are needed to further improve the quantitative understanding of the driving forces of the European land carbon balance.

A Degradation Diagnosis Method for Gas Turbine - Fuel Cell Hybrid Systems Using Bayesian Networks

2021 ◽  
Author(s):  
Luca Mantelli ◽  
Valentina Zaccaria ◽  
Mario L. Ferrari ◽  
Konstantinos G. Kyprianidis
Keyword(s):  
Fuel Cell ◽  
Gas Turbine ◽  
Hybrid Systems ◽  
Hybrid System ◽  
Bayesian Belief Network ◽  
Bayesian Belief Networks ◽  
Belief Networks ◽  
Essential Information ◽  
Belief Network ◽  
Starting Point

Abstract This paper aims to develop and test Bayesian belief network based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor and fuel cell in a hybrid system on the basis of different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks is generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks to fuel cell - gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in gas turbine, fuel cell and sensors in a fuel cell - gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.

A Degradation Diagnosis Method for Gas Turbine – Fuel Cell Hybrid Systems Using Bayesian Networks

2020 ◽  
Author(s):  
L. Mantelli ◽  
V. Zaccaria ◽  
K. Kyprianidis ◽  
M. L. Ferrari
Keyword(s):  
Fuel Cell ◽  
Gas Turbine ◽  
Hybrid Systems ◽  
Hybrid System ◽  
Cell Hybrid ◽  
Bayesian Belief Network ◽  
Bayesian Belief Networks ◽  
Belief Networks ◽  
Sensor Fault ◽  
Belief Network

Abstract During the last decades there has been a rise of awareness regarding the necessity to increase energy systems efficiency and reduce carbon emissions. These goals could be partially achieved through a greater use of gas turbine - solid oxide fuel cell hybrid systems to generate both electric power and heat. However, this kind of systems are known to be delicate, especially due to the fragility of the cell, which could be permanently damaged if its temperature and pressure levels exceed their operative limits. This could be caused by degradation of a component in the system (e.g. the turbomachinery), but also by some sensor fault which leads to a wrong control action. To be considered commercially competitive, these systems must guarantee high reliability and their maintenance costs must be minimized. Thus, it is necessary to integrate these plants with an automated diagnosis system capable to detect degradation levels of the many components (e.g. turbomachinery and fuel cell stack) in order to plan properly the maintenance operations, and also to recognize a sensor fault. This task can be very challenging due to the high complexity of the system and the interactions between its components. Another difficulty is related to the lack of sensors, which is common on commercial power plants, and makes harder the identification of faults in the system. This paper aims to develop and test Bayesian belief network based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor and fuel cell in a hybrid system on the basis of different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks is generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks to fuel cell - gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in gas turbine, fuel cell and sensors in a fuel cell – gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.

Sign in / Sign up

Export Citation Format

Share Document