Bayesian Belief Network Model Research Articles

How Do Developers Influence the Transaction Costs of China’s Prefabricated Housing Development Process? An Investigation through the Bayesian Belief Network Approach

The implementation of prefabricated housing (PH) has become prevalent in China recently due to its advantages in enhancing production and energy-saving efficiency within the construction system. However, stakeholders may not always fully realize the benefits of adopting PH due to the emergence of transaction costs (TCs) in the development process of such projects. This study investigated the strategies for developers to make rational choices for minimizing the TCs of the PH project considering their own attributes and external constraints. A Bayesian Belief Network model was applied as the analytical method, based on surveys conducted in China. A single sensitivity analysis indicated that developers influence the TCs of PH through the following three most impactful factors: prefabrication rate, PH experience, and contract payment method. Integrated strategies are recommended for developers in various situations based on a multiple sensitivity analysis. Developers facing challenges due to high prefabrication rates are advised to reduce the risks by procuring highly qualified general contractors and adopting unit-price contracts. For developers with limited PH experience, adopting the Engineering–Procurement–Construction procurement method is the most efficient way to reduce their TCs in the context of China’s PH market. This study contributes to the current body of knowledge concerning the effect of traders’ attributes and choices on TCs, expanding the application of TC theory and fulfilling the study on the determinants of TCs in construction management.

Modeling prevention behaviors during the COVID-19 pandemic using Bayesian belief networks and protection motivation theory.

Prevention behaviors are important in mitigating the transmission of COVID-19. The protection motivation theory (PMT) links perceptions of risk and coping ability with the act of adopting prevention behaviors. The goal of this research is to test the application of the PMT in predicting adoption of prevention behaviors during the COVID-19 pandemic. Two research objectives are achieved to explore motivating factors for adopting prevention behaviors. (1) The first objective is to identify variables that are strong predictors of prevention behavior adoption. A data-driven approach is used to train Bayesian belief network (BBN) models using results of a survey of participants reporting risk perceptions and prevention behaviors during the COVID-19 pandemic. A large set of models are generated and analyzed to identify significant variables. (2) The second objective is to develop models based on the PMT to predict prevention behaviors. BBN models that predict prevention behaviors were developed using two approaches. In the first approach, a data-driven methodology trains models using survey data alone. In the second approach, expert knowledge is used to develop the structure of the BBN using PMT constructs. Results demonstrate that trust and experience with COVID-19 were important predictors for prevention measure adoption. Models that were developed using the PMT confirm relationships between coping appraisal, threat appraisal, and protective behaviors. Data-driven and PMT-based models perform similarly well, confirming the use of PMT in this context. Predicting adoption of social distancing behaviors provides insight for developing policies duringpandemics.

Integrated modelling of the determinants of household food insecurity during the 2020–2021 COVID-19 lockdown in Uganda

BackgroundThe determinants of household food insecurity (HFI) do not act in isolation, and are known to be complex, stochastic, nonlinear, and multidimensional. Despite this being especially true in periods of shocks, studies that focus on integrated modelling of the HFI determinants during the COVID-19 lockdown are scarce, with no available evidence on Uganda. The main objective of this study was to develop Bayesian belief network (BBN) models to analyse, rank, and illustrate the conceptual reasoning, and complex causal relationships among the determinants of HFI during the COVID-19 lockdown. This study was based on seven rounds of Uganda’s High-Frequency Phone Surveys data sets collected during the lockdown. A total of 15,032 households, 17 independent determinants of HFI, and 8 food security indicators were used in this study. Metrics of sensitivity, and prediction performance were used to evaluate models’ accuracy.ResultsEight BBN models were developed for each food insecurity indicator. The accuracy rates of the models ranged between 70.5% and 93.5%, with an average accuracy rate of 78.5%, indicating excellent predictive performance in identifying the determinants of HFI correctly. Our results revealed that approximately 42.2% of the sampled households (n = 15,032) in Uganda were worried about not having enough food. An estimated 25.2% of the respondents reported skipping a meal, while 32.1% reported consuming less food. Less than 20% of the households experienced food shortage, hunger, or having nothing to eat. Overall, 30.6% of the households were food insecure during the lockdown. The top five ranked determinants of HFI were identified as follows: (1) households’ inability to produce enough food; (2) households’ inability to buy food; (3) reduced household income; (4) limited cash assistance, and (5) households’ inability to stock adequate food supplies.ConclusionsRanking, rather than the statistical significance of the determinants of HFI, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted interventions within the constraints of limited funding. These findings emphasize the importance of intervening on the most highly ranked determinants of HFI to enhance the resilience of local food systems, and households’ capacity to cope with recurring and unforeseen shocks.

Spatiotemporal Patterns and Drivers of Trade-Offs and Synergy in the Beijing–Tianjin Sand Source Control Project: A Bayesian Belief Network-Based Analysis

Understanding the interactions between ecosystem services is the foundation for optimizing ecosystem management and improving human well-being. However, studies on the driving mechanism of ecosystem service relationship formation in arid and semiarid climates are scarce. The Beijing–Tianjin Sand Source Control Project (BTSSCP) has been underway for more than 20 years (2001–2022), and a comprehensive scientific assessment of the effects of its implementation is important for managing ecosystems more efficiently. Taking the BTSSCP region as a study area, four ecosystem services (water conservation (WC), soil conservation (SC), wind erosion control (WEC), and net primary productivity (NPP)) were quantified and mapped in 2000, 2010, and 2020. In this study, a Bayesian belief network (BBN) model was used to analyze ecological processes and determine the relationship between the potential influencing factors and ecosystem services. A sensitivity analysis identified the key factors affecting ecosystem service supply on the basis of a Bayesian belief network simulation. The results showed an increasing trend for four ecosystem services over the past 20 years. Regarding spatial distribution, WC, SC, and NPP exhibited an overall “high in the east and low in the west” pattern, while the spatial distribution of WEC was more dispersed. The intensity of the trade-offs among WC, SC, and NPP has increased, while that of the trade-offs between the rest of the variables has decreased in the BTSSCP over the past 20 years. The results of the Bayesian network modeling indicated that precipitation, NDVI, land use, and temperature were the major variables influencing the strength of ecosystem service trade-offs. The conditional probabilities of the key variables in different states showed that the Sunit Left Banner, Sunit Right Banner, and other areas of control of the desertification of arid grassland had a high probability of trade-offs in WC_SC and SC_NPP. However, the probability of a trade-off between WEC and NPP was higher in the southeastern part of the Yanshan Hills Mountain Water Source Reserve than in the other regions; thus, it should be prioritized as an area for ecological restoration in future planning. This paper provides a scientific reference for the effective protection of ecosystems and the formulation of sustainable policies.

Developing a Bayesian belief network model for quantifying and predicting claim causes-related schedule delays

Nowadays, construction claims have become an inevitable part of the construction project, and they have a serious impact on the project schedule. This study aims to develop a Bayesian belief network (BBN) model for quantifying and predicting claim cause-related schedule delays in Vietnam. Based on data from survey questionnaires and interviews with experts, 23 critical claim causes of construction project schedule delays and 17 significant cause-effect relationship pairs were found and used to build the BBNs models. The results obtained from the BBNs models are good at quantifying the probability of schedule delays in three different case study projects. In the case study of a transportation project, for example, there is a major delay of 34 months, which is about 38.64% of the original length of the project. The model predicted that there is a 69% chance that the project’s schedule delay will be more than 20% of the original length. Using these BBNs models, industry practitioners can prevent claim causes-related schedule delays early. This approach is general and can be applied to any construction project in other countries with small adjustments, especially in the case of developing countries similar to Vietnam.

Application of a Geochemically Informed Leak Detection (GILD) Model to CO2 Injection Sites on the United States Gulf Coast

The Gulf Coast region possesses great potential for CO2 enhanced oil recovery (EOR) and CO2 storage. A geochemically informed leak detection (GILD) model has been applied to CO2 injection sites on the Gulf Coast with considerations of measurement variability. The Jasper aquifer in Montgomery County, Texas, was chosen to demonstrate the method. Based on background data from wells in the area, combinations of mineral and fluid compositions were used to create 23 scenarios for the geochemical model. The output from the geochemical model was used to identify sensitive monitoring species, and response functions were generated for these as a function of the CO2 leakage concentration. The sources of measurement variability for background conditions were characterized from the Jasper aquifer background data, and then normalized using the coefficient of variation of each species across the monitoring wells. Bayesian belief network (BBN) models were constructed, and measurement variability of different levels were added to compare leak detection probabilities. Increasing measurement variability decreased the power to detect a leak of a given size. For a moderately high CO2 concentration of 0.2 mol/kg, the probability of detecting this leakage effect using pH as the monitoring variable in an aquifer with calcite decreases from 98% (no measurement variability) to 61% (medium variability) to 33% (high variability). The loss in power of the sampling protocol with increasing measurement variability is similar in magnitude when Ca2+ or HCO3− is used as the monitoring parameter, but only for aquifers with calcite.

Risk Modeling Framework for Strategic and Operational Intervention to Enhance the Effectiveness of a Closed-Loop Supply Chain

In a closed-loop supply chain (CLSC), acquiring end-of-life vehicles (ELVs) and their components from both primary and secondary markets has posed a huge uncertainty and risk. Moreover, the constant supply of ELV components with minimization of cost and exploitation of natural resources is another pressing challenge. To address the issues, the present study has developed a risk simulation framework to study market uncertainty/risk in a CLSC. In the first phase of the framework, a total of 12 important variables are identified from the existing studies. The total interpretive structural model (TISM) is used to develop a causal relationship network among the variables. Then, Matriced Impacts Cruoses Multiplication Applique a un Classement is used for determining the nature of relationships (i.e., driving or dependence power). In the second phase, the relationship of TISM is used to derive a Bayesian belief network model for determining the level of risks (i.e., high, medium, and low) associated with the CLSC through the generation of conditional probabilities across 1) multi-, 2) single-, and 3) without-parent nodes. The study findings will help decision-makers in adopting strategic and operational interventions to increase the effectiveness and resiliency of the network. Furthermore, it will help practitioners to make decisions on change management implementation for stakeholders' performance audits on the attributes of the ELV recovery program and developing resilience in the CLSC network. Overall, the present study holistically contributes to a broader investigation of the implications of strategic decisions in automobile manufacturers and resellers.

Consequence assessment of gas pipeline failure caused by external pitting corrosion using an integrated Bayesian belief network and GIS model: Application with Alberta pipeline

Consequence assessment of gas pipeline failure caused by external pitting corrosion using an integrated Bayesian belief network and GIS model: Application with Alberta pipeline

A trade-off between farm production and flood alleviation using land use tillage preferences as a natural flood management (NFM) strategy

Trade-off studies effectively compare rational decisions when choosing alternatives. This study utilizes a Bayesian belief network (BBN) model to analyse land use tillage practices for flood management, considering climate, soilscape, slope, and farming systems. The BBN comprises three sub-models using soil samples, farm surveys, synthetic datasets, and literature review data. In one scenario, conventional tillage on a 3° slope increased the net value of crop yield (50.85 %) and positive farm effects (49.64 %) but increased surface runoff (66.24 %) and reduced flood alleviation benefits (58.56 %). On the other hand, conservational tillage on a 3 ° slope yielded lower crop yield increase (14.11 %) and farm production effects (13.80 %) but reduced surface runoff (51.05 %) and increased flood alleviation benefits (45.06 %). Similarly, conventional tillage on a 12 ° slope showed similar crop yield and farm production effects, with slightly higher surface runoff (66.88 %) and reduced flood alleviation benefits (59.13 %). Conversely, adopting conservational tillage on a 12 slope resulted same extent of reduction in crop yield increase band (14.11 %) and farm production effects (13.80 %) but effectively reduced surface runoff (50.42 %) and improved flood alleviation benefits (44.49 %). Therefore, a trade-off between farm production and flood alleviation was identified when tillage preference was applied as a natural flood management strategy. Results showed this trend was particularly pronounced amongst soils on slopes. The model can help users in informed decisions on tillage for sustainable farming, with the potential for improvement through additional variables and farm-specific data.

Using a Bayesian Belief Network to detect healthcare fraud

Healthcare fraud detection algorithms are mostly based on applying machine learning methods to payer-claims data transactions to detect fraudulent activities. However, claim transactions are often analyzed in isolation, disregarding the interconnections that naturally exist in the claims generated from a single unit such as the patient, payer, or provider. Fraud detection frameworks have untapped potential to aid downstream processes such as investigations and audits through transparent and interpretable machine learning models.In this study, we show how fraud detection frameworks can benefit from use of a graphical network model called Bayesian Belief Network (BBN) which exploits the relational structure of attributes in a transaction and has better interpretability properties than many competing machine learning methods. BBN model performance is found to be comparable to commonly utilized baseline models such as logistic regression and random forest after validation with a real-life insurance claims dataset. In particular, the F1 score of fraud class for the best performing BBN was 0.15. The proposed BBN model is also considered to be interpretable to auditors and investigators based on common sense qualitative evaluation.

Time-series forecasting of road distress parameters using dynamic Bayesian belief networks

PurposeTo proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.Design/methodology/approachWhile Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.FindingsThe four developed performance prediction models achieved an overall precision and reliability rate of over 80%.Originality/valueThe proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

Bayesian belief network modelling approach for predicting and ranking risk factors for malaria infections among children under 5 years in refugee settlements in Uganda

BackgroundMalaria risk factors at household level are known to be complex, uncertain, stochastic, nonlinear, and multidimensional. The interplay among these factors, makes targeted interventions, and resource allocation for malaria control challenging. However, few studies have demonstrated malaria’s transmission complexity, control, and integrated modelling, with no available evidence on Uganda’s refugee settlements. Using the 2018–2019 Uganda’s Malaria Indicator Survey (UMIS) data, an alternative Bayesian belief network (BBN) modelling approach was used to analyse, predict, rank and illustrate the conceptual reasoning, and complex causal relationships among the risk factors for malaria infections among children under-five in refugee settlements of Uganda.MethodsIn the UMIS, household level information was obtained using standardized questionnaires, and a total of 675 children under 5 years were tested for malaria. From the dataset, a casefile containing malaria test results, demographic, social-economic and environmental information was created. The casefile was divided into a training (80%, n = 540) and testing (20%, n = 135) datasets. The training dataset was used to develop the BBN model following well established guidelines. The testing dataset was used to evaluate model performance.ResultsModel accuracy was 91.11% with an area under the receiver-operating characteristic curve of 0.95. The model’s spherical payoff was 0.91, with the logarithmic, and quadratic losses of 0.36, and 0.16 respectively, indicating a strong predictive, and classification ability of the model. The probability of refugee children testing positive, and negative for malaria was 48.1% and 51.9% respectively. The top ranked malaria risk factors based on the sensitivity analysis included: (1) age of child; (2) roof materials (i.e., thatch roofs); (3) wall materials (i.e., poles with mud and thatch walls); (4) whether children sleep under insecticide-treated nets; 5) type of toilet facility used (i.e., no toilet facility, and pit latrines with slabs); (6) walk time distance to water sources (between 0 and 10 min); (7) drinking water sources (i.e., open water sources, and piped water on premises).ConclusionRanking, rather than the statistical significance of the malaria risk factors, is crucial as an approach to applied research, as it helps stakeholders determine how to allocate resources for targeted malaria interventions within the constraints of limited funding in the refugee settlements.

Exploring Global Competitiveness Index 4.0 through the lens of country risk

The Global Competitiveness Index (GCI) 4.0 provides an annual evaluation of country-level productivity and development in the fourth industrial revolution era. The GCI 4.0 is classified into 12 pillars representing socioeconomic, financial, health, technological, and other factors of production. Country risk, which is an aggregate measure of country-level exposure to financial, political, and economic risks, can influence national development. This study explores the association between country risk and competitiveness by mapping country risk drivers to GCI and GCI pillars to country risk. Two Bayesian Belief Network models, with a minimum prediction accuracy of 81.3 %, are developed using two data-sets representing the GCI and country risk ratings of 107 countries. Sensitivity analysis reveals that low performance in information and communication technology (ICT) adoption (monetary policy) can significantly affect country risk (GCI), whereas innovation capability (financial system risk) and institutions (legal system) can considerably improve country risk (GCI). By delving into how the pillars of competitiveness interact with various risk factors such as political stability, economic volatility, and regulatory environment, decision-makers, investors, and policymakers can gain valuable insights that guide effective resource allocation, risk mitigation strategies, informed policy formulation, and sustainable development efforts.

Accommodating uncertainty in soil erosion risk assessment: Integration of Bayesian belief networks and MPSIAC model

Accommodating uncertainty stands as one of the most salient challenges in the development of soil erosion assessment tools. We presented a novel approach integrating the Modified Pacific Southwest Inter-Agency Committee (MPSIAC) model and Bayesian Belief Networks (BBNs) to assess soil erosion in a region of western Iran. The soil erosion status was reckoned based on the nine factors of MPSIAC. We utilized BBNs to produce a causal model for soil erosion, with output probabilities being validated through re-evaluation and sensitivity analysis. We identified erosion types, geological formations, run-off, soil erodibility, soil permeability, soil characteristics, and precipitation intensity as the main determinants of soil erosion. A significant, positive correlation existed between the erosion rate derived from MPSIAC and BBNs model in all land-use/covers over the work units. Overall, this study highlighted the potential of BBNs as a supportive tool for soil erosion prediction as well as a relatively simple and updatable soil erosion model for dealing with the diagnostic, scenario, and sensitivity analysis. Considering the increasing incidence of soil erosion, the BBNs model proposed in this study can be extended to a variety of ecosystems that are subject to soil erosion and changes in the probability of its causal factors.

Exploring network-based dependencies between country-level sustainability and business risks

The concept of sustainability has gained prominence in the global discourse, with increasing recognition of its impact on business risk. By integrating sustainability considerations into their strategies and operations, national policymakers and businesses can mitigate business risks and contribute to national sustainability goals. The objective of this study is to explore dependencies among the risks associated with the 17 Sustainable Development Goals (SDGs), introduced by the United Nations in 2015, and country-level business risk. In particular, this study aims to identify critical SDGs that can influence business risk based on the two extreme performance states (i.e., best and worst scenarios) of individual SDGs. A data-driven Bayesian Belief Network model is developed using two datasets on country-level sustainability performance and business risk assessment. On the one hand, ‘quality education’, ‘no poverty’, ‘affordable and clean energy’ and ‘sustainable cities and communities’ are identified as the most critical SDGs based on the negative impact of their associated risks on business risk. On the other hand, low risk (high performance) associated with the ‘peace, justice and strong institutions’, ‘life on land’ and ‘industry, innovation and infrastructure’ SDGs can reduce business risk. Strong dependencies are found between individual SDG risks and business risk, implying policymakers need to consider the implications of their sustainability initiatives on the risks and uncertainties surrounding their country's business environment.

Bayesian belief network model for quantifying the delay risk of marine construction projects in developing countries

Bayesian belief network model for quantifying the delay risk of marine construction projects in developing countries

A data-driven Bayesian belief network model for exploring patient experience drivers in healthcare sector

A data-driven Bayesian belief network model for exploring patient experience drivers in healthcare sector

Failure assessment of oil and gas transmission pipelines using an integrated Bayesian belief network and GIS model

Corrosion is one of the main reasons for pipeline failure in the oil and gas industry. Because a pipeline failure can result in serious personal injury, monetary loss, and environmental damage, pipeline operators need to make timely, and cost-effective decisions to prevent accidents in high consequence areas. The majority of models proposed in this area are designed and computed for individual pipe segments, with only a limited number of studies considering the system level. The system level pertains to spatially distributed pipelines that span hundreds of miles across different environmental conditions. The current study propose combining GIS and Bayesian belief network to determine the probability of failure of transmission pipelines due to external pitting corrosion. The model incorporates data from an extensive network of pipes spanning hundreds of miles and their surroundings to compute the failure probability of the pipeline infrastructure. The combination of spatial GIS capabilities with the reasoning capabilities of the Bayesian network provides a powerful tool to estimate the likelihood of transmission pipelines failing in a specific area, based on the available information.

A geochemically informed leak detection (GILD) model for CO2 injection sites

Early detection of CO2 leakage through monitoring is important to ensure long-term safety for geologic carbon storage (GCS). A geochemically informed leak detection (GILD) model has been developed for groundwater chemistry monitoring at CO2 injection sites. The GILD model integrates a geochemical model that simulates fluid chemistry changes in CO2 leakage events and a Bayesian belief network (BBN) model that evaluates monitoring observations to identify leakages. The geochemical model is implemented using Geochemists’ Workbench to assess fluid chemistry changes as a result of small CO2 leakage in an above-zone monitoring interval (AZMI) formation with varying mineral assemblages and background fluids. Response functions are fitted to the output of the geochemical model and are translated to conditional probabilities in the BBN model. The BBN model gives operational prediction of the leak probability given a set of groundwater monitoring measurements and the probability of detecting a leak at a given magnitude. The detection capabilities of multiple monitoring parameters are compared. For aquifers that contain calcite, it is valuable to incorporate other monitoring parameters with pH to increase the sensitivity of detection. For aquifers with no calcite, pH alone is a sensitive parameter. This research illustrates a method of identifying CO2 leakage into aquifers with both geochemical and statistical tools.

Analysis of Carbon Emissions and Influencing Factors in China Based on City Scale

Assessing regional carbon emissions and their relationship with socio-economic conditions is very important for developing strategies for carbon emission reduction. This study explored the impact of the proportion of non-fossil energy, the land development degree, the urbanization rate of permanent residents, the proportion of secondary industry, per capita GDP, and per capita construction land area on per capita CO2 emissions in 339 prefecture-level and above cities in China (excluding some cities in Xinjiang, Hong Kong, Macao, and Taiwan). A Bayesian belief network modeling carbon emissions was constructed to identify the global effects of various factors on per capita CO2 emissions, and multiscale geographically weighted regression was used to analyze their local effects. The results showed that first, per capita CO2 emissions of cities in China increased from the south to the north and decreased from the eastern coast to the inland region. Second, globally, the sensitivity of per capita CO2 emissions to various factors from high to low was in the order of per capita construction land area>per capita GDP>urbanization rate of permanent residents>land development degree>proportion of secondary industry>proportion of non-fossil energy. Third, locally, the direction of the spatial relationship between each factor and per capita CO2 emissions was consistent with the global relationship, and there was spatial heterogeneity in the strength of the relationship. Finally, clean energy, decarbonization technologies, saving and intensive use of land, and green living were effective ways to achieve the dual-carbon goal.