Knowledge Discovery for Query Formulation for Validation of a Bayesian Belief Network

Water mains are the essential component of a water supply system (WSS) buried underground, and they account for 80% of total system expenditures. Deterioration of this asset, as a result of an aggressive soil environment, is inevitable over a pipe’s service life. Although an increasing number of studies have estimated the remaining service life (RSL) of metallic pipes, most of them either are data intensive or consider limited soil and pipe parameters. In this paper, a Bayesian belief network (BBN) model is proposed to handle the problem of varying data availability and the dependency between parameters. First the proposed approach uses a combination of empirical data, experimental data, expert opinion, and a mathematical model to predict soil corrosivity and pit depth. Then a simple programming logic is used to predict RSL. Finally, the performance of the model is evaluated using a BBN sensitivity analysis. Monte Carlo simulations (MCSs) are performed using randomly generated input from measured ...

Climate change is a pervasive global issue that threatens the livelihoods and wellbeing of billions living globally. Climate change is a risk multiplier impacting all ecosystems, society, and sectors of the economy. The agriculture sector is one such sector that is highly vulnerable to changes in climate. In a country like Nepal where rainfed agriculture is a dominant occupation and a key pillar of the country’s economy, climate change brings risks and negative consequences for on-farm production, farmers' livelihoods as well as on the country’s development. The impacts of climate change including rising temperatures, an increase in the frequency or intensity of extreme weather events such as drought, and shifts in the rainfall seasonality, can cause a decline in food production and threaten the quality of food supplies, leading to reduced food security. The rise in the global population will increase global demand for food which implies that agriculture needs to boost production and increase yields, among other things. The unprecedented risks posed by climate change potentially undermine the ability of farms and farm holders to grow adequate and quality food. The severity of these risks varies due to a range of underlying factors including low economic development, their location, existing biophysical and socioeconomic conditions, and institutional arrangements. While the impacts of climate change on food production as well as agricultural practices in Nepal have been documented, there is a dearth in scholarly literature that has assessed the impacts of climate change on household food security in Nepal incorporating farmers’ perspectives and in particular smallholder subsistence farmers. Furthermore, there is only modest literature that has examined geographical variations in those experiences and understandings. This PhD study aimed, therefore, to investigate the effects of climate change on agricultural practices and food security, with a focus on subsistence smallholder farmers in three main agro- ecological zones of Nepal known as The Mountains, Hills, and the Terai. The study aimed to respond to the primary research question: How are Nepalese farming communities being impacted by climate change and how are they responding to ensure their continued food security? To answer this main research, question the study posed the following secondary research questions: Q1. How is climate changing and how is it impacting subsistence agriculture? Q2. What are the farmers experiencing and what is their understanding? a. Are there gender differences in understanding and experiencing? Q3. What is the state of food insecurity among these farmers? a. How is it being impacted by climate change? Q4. What adaptation strategies have been adopted by smallholder farmers to address threats to agricultural practices and food security from climate change and other pressures? Both the qualitative and qualitative data were collected using multiple methods to address the identified research questions. Methods included a narrative literature review, systematic review, face to face interviews with farmers, individual interviews with key informants and focus groups with the women's group. Climate data on temperature between 1971-2013 and rainfall between 1967-2013 were analysed. Additionally, secondary data on crop yield from 1980 to 2016 were also analysed to gain a better empirical understanding of the relations between climate change and yield pattern and to triangulate and validate the findings from the interviews. Quantitative data on cereal crop yields and climate data were systematically tabulated and further statistically analysed using software R. This study employed the Bayesian approach to statistical modelling. Besides, this study undertook an integrated risk assessment of food insecurity using the Bayesian Belief Network model to reflect how the risk of food insecurity is influenced under two scenarios: (1) current climate conditions and (2) the influences of different adaptation strategies employed. NVivo was used for content analysis for qualitative data obtained from the key informant interview and focus group data and analysis of transcripts from farmers' structured interviews. The findings showed that agricultural practices have undergone various changes over the past 30 years. Climate change impacts were experienced by farmers in all three agro-ecological zones of Nepal. However, the impacts varied between these zones in terms of frequency and intensity. The effect of climate change was highly pronounced in the Mountains zone followed by the Hills and the Terai. The results confirmed that rural subsistence smallholder farmers dependent on rainfed agriculture are vulnerable to climate change impacts. Moreover, it disproportionately affects the poor farmers whose income hinges solely on agriculture and associated activities. Boosting agricultural production and empowering these smallholders is key to enhancing their food security. The experiences reported by farmers are well aligned with the trend of the climatic variables obtained from climate data analysis, highlighting the importance of perception-based survey in complementing climate research. The study demonstrated both the climatic and non-climatic factors are affecting agricultural practices as well as household food security of these farming communities. It is, therefore, difficult to isolate the influence of any of these factors. This was supported by the findings from the risk assessment undertaken by Bayesian modelling. Based on Bayesian modelling, the smallholder farmers mainly at the Mountain zone were at the risk of food insecurity. The measures to increase the adaptive capacity of these smallholders were found to help them manage the risk of food insecurity. Addressing the complicated and multifaceted concerns of climate change and food security needs multidisciplinary and multisectoral adaptation interventions acknowledging underlying biophysical, social, economic, geographical and environmental circumstances. Farmers have taken some actions to adapt and reduce the worsening impacts of climate change. Nevertheless, farmers encountered several barriers in effectively adapting to climate change. This study concluded there is an urgent need for a transformative level of intervention that warrants a coordinated action and collaboration between relevant stakeholders working in this field, including governments and non-governmental organizations, to target the most vulnerable and the needy smallholder farmers addressing the constraints and pressures they face. Policy and decision-makers should work extensively and sensitively with smallholders to ensure the maintenance of their livelihood and to guarantee their food security. Combining local and scientific knowledge is needed to help direct research and tailor adaptation solutions that meet local conditions and needs.

Background: Short (<6hrs) and long (>8 hrs) sleep durations are linked to stroke and cardiovascular disease. However, results have relied primarily on regression analysis, which may not be optimal to model associations between sleep and medical outcomes. Big-data and machine learning analyses provide unique opportunities to quantify dynamic interactions between sleep and medical outcomes, adjusting for multiple risk factors. Method: The current study utilized two types of analyses: logistic regression and Bayesian Belief Network, a form of machine learning analysis, to assess sleep-related stroke risk. We used data from the 2004-2013 National Health Interview Survey, yielding 288,888 cases, to investigate how short (<7hrs.) and long (>8hrs.) sleep durations impact stroke risk. In both analyses, we assessed the contribution of 34 demographic, medical, behavioral, and psychosocial factors. We used SPSS 20 to conduct regression analyses and BayesiaLab’s Tree Augmented Naïve Bayes learning algorithm for complex system analysis. We compared results of both analytic models to determine their ecological and clinical utility. Results: Forty-eight percent of volunteers were ≤45 yrs.; 77.40% were White; 15.96%, Black/African American; and 45.1% made < $35K annually; 29.55% reported short sleep and 8.9%, long sleep; 61.55% were average sleepers (7-8hrs.). Adjusted regression models indicated that relationships between short sleep and stroke were not significant (OR=.97, 95% CI=0.92-1.02, p =.21); however, long sleep was associated with stroke (OR=1.43, 95% CI= 1.32-1.52, p <.001). Results from Bayesian analysis indicated both short and long sleep were associated with stroke, but that long sleep doubled stroke risk (7.48%) relative to short sleep (3.74%). Regression model had a R 2 of 0.24 for short sleep and long sleep, while the R 2 for Bayesia was 0.73. Conclusion: Bayesian Belief Network analysis is superior to regression modeling because it provides ecologically and clinically valid findings. Although both short and long sleep durations are associated with stroke risk, long sleep seems to be a stronger predictor.

Bayesian belief networks to analyse trade-offs among ecosystem services at the regional scale

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.

Pregnancy and childbirth are commonly seen as positive experiences, but they can also pose distinct challenges and risks, especially when care is insufficient. This study investigates the factors influencing maternity patient experience by exploring the complex interactions among these factors. Using data from the 2021 maternity patient survey by the National Health Services (NHS) in England, we implemented a Bayesian Belief Network (BBN) to model these interactions. Three structural learning models were created, namely Bayesian Search (BS), Peter-Clark (PC), and Greedy Thick Thinning (GTT). Further, sensitivity analysis was conducted to quantify interactions among the influencing factors and identify the most influential factor affecting the outcome. The results underscore the importance of recognizing the interdependencies among the eight key domains of the survey, which collectively shape maternity care experiences. These factors include the start of care in pregnancy, antenatal check-ups, care during pregnancy, labour and birth, staff caring, care in the hospital, feeding the baby, and care after birth. These findings can guide healthcare managers and decision-makers in developing proactive strategies to mitigate factors impacting maternity patient experiences. Ultimately, this study contributes to the ongoing efforts to enhance the quality of maternity care and improve outcomes for mothers and their infants.

A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland

This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that were developed by ML algorithms-K2, hill climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning in Netica, thereby fixing the BBN models. The performance measure indexes, namely, overall accuracy (OA), precision, recall, F-measure, and area under the receiver operating characteristic curve, were used to evaluate the training and testing BBN models’ performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models. The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors, whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential. The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.

Liquefaction is considered a damaging phenomenon of earthquakes and a major cause of concern in civil engineering. Therefore, its predictory assessment is an essential task for geotechnical experts. This paper investigates the performance of Bayesian belief network (BBN) and C4.5 decision tree (DT) models to evaluate seismic soil liquefaction potential based on the updated and relatively large cone penetration test (CPT) dataset (which includes 251 case histories), comparing them to a simplified procedure and an evolutionary-based approach. The BBN model was developed using the K2 machine learning algorithm and domain knowledge (DK) with data fusion methodology, while the DT model was created using a C4.5 algorithm. This study shows that the BBN model is preferred over the others for evaluation of seismic soil liquefaction potential. Owing to its overall performance, simplicity in practice, data-driven characteristics, and ability to map interactions between variables, the use of a BBN model in assessing seismic soil liquefaction is quite promising. The results of a sensitivity analysis show that ‘equivalent clean sand penetration resistance’ is the most significant factor affecting liquefaction potential. This study also interprets the probabilistic reasoning of the robust BBN model and most probable explanation (MPE) of seismic soil liquefied sites, based on an engineering point of view.

Influences of variables on ship collision probability in a Bayesian belief network model

Naive Bayesian belief network modeling is applied to direct numerically simulated imagery of oscillatory sedimentladen flow to illustrate the feasibility of creating a system model which captures the statistical interrelationship of the surface layer sediment concentration, pressure, and vertical velocity eddy scales with the sub-surface Reynolds stress. From a prognostic reasoning viewpoint, preliminary model results suggest that large sediment concentration eddy scales may result from the application of large positive Reynolds stress. However, from a diagnostic reasoning viewpoint, initial results suggest that robustly inferring sub-surface boundary layer stress from surface sediment concentration eddy scales may be a difficult task. The model formulism used allows for the ability to statistically characterize flow structure at depth from observations taken across a surface boundary layer, making the results relevant to image analysis at the airsea interfacial boundary layer in large-scale coastal and riverine systems.

The continuous growth in population and consumption, the intensity of competition for land, water and energy and the overexploitation of the ecosystem have all affected Africa's ability to sustain its food security and natural resources. In recent years, many promising agricultural development initiatives were unable to provide sustainable solutions to agricultural challenges in most parts of Africa, including Ghana, as a result of policy failures. The agricultural sector is a complex system and requires a holistic approach to deal with the root causes of challenges. This research therefore uses systems thinking tools, including causal loop diagrams and Bayesian belief network modelling, to develop new structural systems models whereby stakeholders can determine the components and interactions between the structure, conduct and performance (SCP) of the agricultural sector in Ghana, by using the first five steps of the Evolutionary Learning Laboratory. The results illustrate how the SCP elements interact together to influence the survival and growth of the agricultural sector. The study identifies that stakeholders adopt several strategies to survive and compete, which lead to overexploitation of the ecosystem. The results from the Bayesian belief network models indicate that the implementation of systemically determined interventions, policies and strategies could significantly improve the probability of business survival and growth from 58.8 to 73%. Also, the chances of improving the SCP could be increased from 39, 28.3 and 36.4 to 80.1, 55.9 and 62.4%, respectively, and these may vary based on the conditional probability tables. This paper contributes to the systemic approach to SCP, in that improvements to production and allocative efficiency may usher in a greater potential for improving food security, supporting the ecosystem and further strengthening agricultural sustainability. Copyright © 2016 John Wiley & Sons, Ltd.

Sustainability is increasingly critical for universities as institutions influence environmental stewardship, social equity, and the management of economic resources. The Environmental, Social, and Governance (ESG) framework is a widely used tool to assess university sustainability efforts, providing insight into institutional policies, practices, and external contributions, such as alumni research and societal engagement. This study analyzes data from 1,397 universities using the 2024 QS (Quacquarelli Symonds) sustainability rankings, which evaluate environmental impact, social impact, and governance indicators. A Bayesian Belief Network (BBN) model is applied to uncover the complex interdependencies among governance, social impact, and environmental impact, revealing how these factors jointly influence overall sustainability performance. The findings demonstrate that governance plays a pivotal role, with strong governance enhancing both social and environmental outcomes. By highlighting these interdependencies, the study provides valuable insights for university leaders and policymakers seeking to optimize ESG-based strategies to improve institutional sustainability. The main contribution of this research lies in applying a network-based probabilistic model to ESG indicators, offering a more nuanced understanding of how governance and other ESG factors interact to shape sustainability performance in higher education.

An investigation of neuro-fuzzy systems in psychosomatic disorders

Dispersive spoil/soil management is a major environmental and economic challenge for active coal mines as well as sustainable mine closure across the globe. To explore and design a framework for managing dispersive spoil, considering the complexities as well as data availability, this paper has developed a Bayesian Belief Network (BBN)-a probabilistic predictive framework to support practical and cost-effective decisions for the management of dispersive spoil. This approach enabled incorporation of expert knowledge where data were insufficient for modelling purposes. The performance of the model was validated using field data from actively managed mine sites and found to be consistent in the prediction of soil erosion and ground cover. Agreement between predicted soil erosion probability and field observations was greater than 74%, and greater than 70% for ground cover protection. The model performance was further noticeably improved by calibration of Conditional Probability Tables (CPTs). This demonstrates the value of the BBN modelling approach, whereby the use of currently best-available data can provide a practical result, with the capacity for significant model improvement over time as more (targeted) data come to hand.