Conceptual Bayesian networks for contaminated site ecological risk assessment and remediation support

Carriger, John F.; Parker, Randy

doi:10.1016/j.jenvman.2020.111478

Cited by 32 publications

(13 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In view of various uncertainties in the future, multiscenario analysis can predict the ecosystem services changes under different land development and provide a scientific basis for regional ecological management (Hu et al, 2018) researchers (Carriger & Parker, 2021). In this study, we first constructed the Bayesian conceptual network of ecosystem services (Figure 3) and then parameterized the conceptual network based on the training set (Figure 6).…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies mostly optimized ecosystem services in the light of land use data (Calder et al, 2019; Caro et al, 2020; Cong et al, 2020; Su et al, 2020), which is unable to reasonably express the uncertainty of key factors and sufficiently reflect causal relationship between factors. As an important decision‐supporting tool, BBN can make inferences for insufficient, incomplete, and uncertain problems under a priori and sufficient sample conditions (van Luu et al, 2009); can express factor uncertainties through CPT (Dai et al, 2021); and can effectively express complex causal relations in ecosystems based on expert knowledge, machine learning, and empirical network models established by previous researchers (Carriger & Parker, 2021). In this study, we first constructed the Bayesian conceptual network of ecosystem services (Figure 3) and then parameterized the conceptual network based on the training set (Figure 6).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Integrating a mixed‐cell cellular automata model and Bayesian belief network for ecosystem services optimization to guide ecological restoration and conservation

Zhou

2022

Land Degrad Dev

View full text Add to dashboard Cite

An ecosystem is a complex system with a large number of dynamic variables, which poses challenges to the optimization of ecosystem services. However, traditional ecosystem services optimization methods do not take into account the complexity and uncertainty of variables. To address this complexity and uncertainty, we propose an innovative approach using a mixed‐cell cellular automata (MCCA) model and a Bayesian belief network (BBN) model for ecosystem service optimization. This approach was applied to the southern region of Sichuan Province, China, using an existing dataset to simulate land use patterns and predict ecosystem services in 2035 under different development scenarios. To achieve ecological restoration and conservation, we also determined the key factor combinations and key ecological regions at various ecosystem service levels. Results showed that ecological protection scenario design has important significance as a reference for maintaining and ameliorating regional ecosystem services and functions. We also identified that the highest level of ecosystem services was mainly located in the areas with the highest net primary productivity (NPP), the highest slope, the highest forestland area, and low ET. According to these findings, some suggestions for ecological restoration and conservation in key regions were put forward. This approach fully considers the uncertainty of factors; therefore, it can be used as an effective tool for designing ecosystem management strategies.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Integrating a mixed‐cell cellular automata model and Bayesian belief network for ecosystem services optimization to guide ecological restoration and conservation

Zhou

2022

Land Degrad Dev

View full text Add to dashboard Cite

show abstract

“…BDNs are frequently used to optimize the decision-making process under uncertainty in disciplines, such as engineering, [78][79][80][81] medicine, [82][83][84][85] biology, 86,87 and information technology. [88][89][90] BDNs are also ideal for reducing risk in the decision-making process because risk is associated with uncertainty.…”

Section: Created the Technique Calledmentioning

confidence: 99%

Predicting locally manageable resource failures of high availability clusters

Somasekaram

Călinescu

2022

Softw Pract Exp

View full text Add to dashboard Cite

Critical services from domains as diverse as finance, manufacturing and healthcare are often delivered by complex enterprise applications (EAs). High-availability clusters (HACs) are software-managed IT infrastructures that enable these EAs to operate with minimum downtime. This paper presents a novel Bayesian decision network model to improve the failure detection capabilities of the HACs components using a comprehensive set of characteristics for the analysed component. The model then combines these characteristics to predict whether the failure of this component can be managed locally at the failed component level without propagating the failure to upper-level components and causing a complete system failure. By improving the detection capabilities and predicting locally manageable failures, the model improves the decision-making process of HACs, and has the potential to reduce the downtime and improve availability for the applications protected by HACs. The model uses the capabilities of the Bayesian decision networks, which combines Bayesian networks with the utility theory, to assign weights to different characteristics and consolidate the related variables to output the result. The model evaluation in a realistic testbed environment with three servers, an established HAC and a well-known EA shows that the model can improve the area under the Receiver Operating Characteristic (ROC) curve for prediction of locally manageable failures by up to 9.05% compared to the baseline HAC results.

show abstract

“…In the field of natural disasters, a typhoon disaster KG is constructed to show the distribution and disaster situation of typhoon disasters, which help people to avoid typhoon disasters [41]. In the ecological field, conceptual Bayesian networks (CBNs) for ecological risk assessment and remediation support at the contaminated sites have also been studied [42]. Some companies have constructed a financial risk KG.…”

Section: Kg and Its Applications In The Risk Fieldmentioning

confidence: 99%

Risk Assessment of Alpine Skiing Events Based on Knowledge Graph: A Focus on Meteorological Conditions

Wang

Zhang

Feng

et al. 2021

IJGI

View full text Add to dashboard Cite

The alpine skiing event is particularly vulnerable to changes in meteorological conditions as a winter sport held outdoors. The commonly used risk assessment methods cannot be inflexible and cannot be dynamically adjusted to combine multiple risk factors and actual conditions. A knowledge graph can organize data resources in the risk domain as structured knowledge systems. This paper combines a knowledge graph and risk assessment to effectively assess the risk status. First of all, we introduce the relevant literature review of sports event risk assessment, combining the characteristics of alpine skiing events. Then, we summarize the risk types of alpine skiing events and related risk knowledge. Secondly, a model is proposed to introduce an event risk assessment model based on the RippleNet framework combined with the characteristics of large-scale sports events. Moreover, the validity of the model is verified. The results show that the RippleNet-based event risk assessment model can be used to assess the risk of alpine skiing events. In order to effectively deal with the large-scale sports events that occur with a variety of risks, the smooth implementation of large-scale sports events provides a strong guarantee.

show abstract

Conceptual Bayesian networks for contaminated site ecological risk assessment and remediation support

Cited by 32 publications

References 32 publications

Integrating a mixed‐cell cellular automata model and Bayesian belief network for ecosystem services optimization to guide ecological restoration and conservation

Integrating a mixed‐cell cellular automata model and Bayesian belief network for ecosystem services optimization to guide ecological restoration and conservation

Predicting locally manageable resource failures of high availability clusters

Risk Assessment of Alpine Skiing Events Based on Knowledge Graph: A Focus on Meteorological Conditions

Contact Info

Product

Resources

About