Bayesian network modeling for ROI simulation and scenario analysis: a case study on commercial aquaponics

In complex industrial environments, uncertainty is inherent in decision-making due to dynamic operating conditions, sensor variability, and the vast heterogeneity of data sources. Traditional deterministic models often fall short in capturing the probabilistic dependencies and hidden causal relationships that characterize these systems. As industries increasingly adopt data-driven strategies, probabilistic reasoning frameworks such as Bayesian Network (BN) modeling have gained prominence for their ability to encode domain knowledge, handle incomplete information, and support transparent inference under uncertainty. Bayesian Networks offer a graphical model-based approach to representing joint probability distributions over multiple interrelated variables. In large-scale industrial datasets—ranging from manufacturing process logs and predictive maintenance records to energy grid telemetry and supply chain metrics—BNs enable efficient reasoning by decomposing complex dependencies into directed acyclic graphs. These structures support not only diagnostic and prognostic tasks but also counterfactual analysis and real-time decision support. This paper explores the methodology and practical application of Bayesian Network Modeling for probabilistic reasoning and risk assessment in industrial contexts. Emphasis is placed on model construction from big data, structure learning from high-dimensional variables, and parameter estimation under noisy or partially missing data. Case studies from fault prediction in chemical processing plants and anomaly detection in smart grid infrastructure illustrate the scalability and interpretability of BNs in practice. The integration of expert knowledge with data-driven inference highlights the hybrid power of Bayesian models in enhancing industrial resilience, safety, and strategic planning.

A multivariate Bayesian network analysis of water quality factors influencing trihalomethanes formation in drinking water distribution systems

This paper investigates the modernization of a corporate practice called scenario analysis. It is widely used across the globe, originating from World War II, helping companies to gain a better understanding of potential future outcomes. In order to modernize scenario analysis, this paper will explore the novel approaches; of a hybrid development between agent-based modelling (ABM) and scenario analysis, the utilization of bottom-up and industry view approaches and introducing elements of human social behaviour. All these features are integrated in a single scenario analysis ABM that will model the fundamental philosophy behind the daily operations of a large corporation, such as Deloitte. This paper addresses more natural evolutions for scenarios, by looking at the firms' fundamental building blocks, rather than just an objective view based on the firms' financial statements. This model will focus upon accurate representations of the daily interactions of an enterprise, especially improving the explainability and transparency of causality in scenario analysis generated by the models, in order to achieve enduring agreements among executives about the firm's future. With this novel approach to scenario analysis, the modelling of a firm with the use of an ABM, there is the potential to provide more in-depth micro-observations of the future, rather than the conventional scenario analysis methods performed today.

Autonomous ferries are providing new opportunities for urban transport mobility. With this change comes a new risk picture, which is characterised to a large extent by the safe transition from autonomous mode to manual model in critical situations. The paper presents a case study of applying an adapted risk assessment method based on the Scenario Analysis in the Crisis Intervention and Operability study (CRIOP) framework. The paper focuses on the applicability of the Scenario Analysis to address the human-automation interaction. This is done by presenting a case study applying the method on a prototype of a Human–Machine Interface (HMI) in the land-based control centre for an autonomous ferry. Hence, the paper presents findings on two levels: a method study and a case study. A concept of operation (CONOPS) and a preliminary hazard analysis lay the foundation for the scenario development, the analysis, and the discussion in a case study workshop. The case study involved a Scenario Analysis of a handover situation where the autonomous system asked for assistance from the operator in a land-based control centre. The results include a list of identified safety issues such as missing procedures, an alarm philosophy and an emergency preparedness plan, and a need for explainable AI. Findings from the study show that the Scenario Analysis method can be a valuable tool to address the human element in risk assessment by focusing on the operators’ ability to handle critical situations.

This paper presents experiences in applying the goal decomposition and scenario analysis model in the context of Business Process Reengineering (BPR). The relationships of goals, scenarios, as well as the understanding and description of business processes are discussed. Different methods of goal refinement, and the application of scenarios to support this process of refining goals and roles are reviewed. A case study is presented which serves to exemplify and validate the process of using scenarios in refining business process descriptions. We tried deriving full scenarios for business processes, but obtaining them from the organization's prescriptive goals was difficult. Explanatory scenarios that justify descriptive goals are easier to obtain but are fragmentary. We conclude that both types of scenario and goal analysis are necessary for effective BPR. The need for technology support for this process is discussed and attention is given to future anticipated research in this area.

Meta-analyses are frequently performed on adverse event data and are primarily used for improving statistical power to detect safety signals. However, in the evaluation of drug safety for New Drug Applications, simple pooling of adverse event data from multiple clinical trials is still commonly used. We sought to propose a new Bayesian hierarchical meta-analytic approach based on consideration of a hierarchical structure of reported individual adverse event data from multiple randomized clinical trials. To develop our meta-analysis model, we extended an existing three-stage Bayesian hierarchical model by including an additional stage of the clinical trial level in the hierarchical model; this generated a four-stage Bayesian hierarchical model. We applied the proposed Bayesian meta-analysis models to published adverse event data from three premarketing randomized clinical trials of tadalafil and to a simulation study motivated by the case example to evaluate the characteristics of three alternative models. Comparison of the results from the Bayesian meta-analysis model with those from Fisher's exact test after simple pooling showed that 6 out of 10 adverse events were the same within a top 10 ranking of individual adverse events with regard to association with treatment. However, more individual adverse events were detected in the Bayesian meta-analysis model than in Fisher's exact test under the body system "Musculoskeletal and connective tissue disorders." Moreover, comparison of the overall trend of estimates between the Bayesian model and the standard approach (odds ratios after simple pooling methods) revealed that the posterior median odds ratios for the Bayesian model for most adverse events shrank toward values for no association. Based on the simulation results, the Bayesian meta-analysis model could balance the false detection rate and power to a better extent than Fisher's exact test. For example, when the threshold value of the posterior probability for signal detection was set to 0.8, the false detection rate was 41% and power was 88% in the Bayesian meta-analysis model, whereas the false detection rate was 56% and power was 86% in Fisher's exact test. Adverse events under the same body system were not necessarily positively related when we used "system organ class" and "preferred term" in the Medical Dictionary for Regulatory Activities as a hierarchical structure of adverse events. For the Bayesian meta-analysis models to be effective, the validity of the hierarchical structure of adverse events and the grouping of adverse events are critical. Our proposed meta-analysis models considered trial effects to avoid confounding by trial and borrowed strength from both within and across body systems to obtain reasonable and stable estimates of an effect measure by considering a hierarchical structure of adverse events.

Bunkering services are complex and require experienced engineers to make bunker barge allocation decisions. We have developed a knowledge-based simulation model to evaluate overall system performance. This knowledge-based simulation integrates artificial intelligence and system simulation. The rationale behind using such a technique is that it is not feasible to use real humans in a simulation due to the availability of human resources and lack of flexibility in scenario analysis. Knowledge-based systems can compensate for human decision-making processes that are absent in simulation models. Thus, the human role can be replaced by an expert system, permitting the synergistic effects of the two modeling approaches to accurately represent the entire system being modeled. The model is capable of quantifying the service quality under various bunker barge allocation scenarios and fleet expansion plans. The more efficient bunkering service plans were acquired and presented using the case study in the Port of Kaohsiung.

Carbon sequestration and income generation are competing objectives in modern forest management. The climate commitments of many countries depend on forests as carbon sinks which must be quantified, monitored, and projected into the future. For projections we need tools to model forest development and perform scenario analyses to assess future carbon sequestration potentials under different management regimes, the expected net present value of such regimes, and possible impacts of climate change. We propose a scenario analysis software tool (GAYA 2.0) that can assist in answering these types of questions using stand level simulations, detailed carbon flow models and an optimizer. This paper has two objectives: (1) to describe GAYA 2.0, and (2) demonstrate its potential in a case study where we analyze the forest carbon balance over a region in Norway based on national forest inventory sample plots. The tool was used to map the optimality front between the carbon benefit and net present value. We observed changes in net present value for different levels of carbon benefit as well as changes in optimal management strategies. We predicted future changes in several forest carbon pools as well as albedo and illustrated the impact of gradual increase in forest productivity (i.e., due to climate warming). Having been updated and modernized from its previous version with increased attention to forest carbon and energy fluxes, GAYA 2.0 is an effective tool that offers multiple opportunities to perform various types of scenario analyses in forest management.

A Bayesian Network model for risk analysis of deepwater drilling riser fracture failure

The article critically analysed the advancements in logistics and their profound influence on the expansion of transportation infrastructure in Europe. Its core objective was to evaluate the extent to which contemporary technological advancements and digital solutions contribute to the enhancement of efficiency, safety, and sustainability within transportation systems. The work focused on analysing the key innovations such as automated transport management, the integration of the Internet of Things (IoT) and the use of big data to determine how they contribute to infrastructure development and what challenges they pose. To achieve the article’s goal, the research was based on an integrated approach that included the analysis of secondary data such as academic articles, reports from government and industry organisations, and case studies. A quantitative analysis was also used in order to assess the impact of innovations on key performance indicators of transport infrastructure. The main methods were the comparative analysis, modeling and scenario analysis, which allowed assessing potential future trends and risks. The investigation was conducted by analyzing analytical reports from logistics companies, synthesising strategies for the development of transport infrastructure, and utilising contemporary scientific methodologies to establish logistics prowess. The use of scientific research methods in the article was determined in the context of the disclosure of key practices, trends and means of the functioning quality of the logistics networks improvement, chains supply and transport infrastructure, as a key source of economic development. The article analysed the theoretical concepts of innovations in logistics and the possibilities of their application in the modern business environment. Attention was paid to issues of applied strategic solutions based on the practice of developed countries, regarding the introduction of innovations in the provision of transport infrastructure and the possibility of its strengthening due to the automation of decisions and effective management. The paper presents the significant outcomes achieved through innovations in logistics and the implementation of logistics projects in Poland and other European countries. These nations have demonstrated active involvement in the development of exceptionally efficient logistics systems.

Lagoons are highly valued coastal environments providing unique ecosystem services. However, they are fragile and vulnerable to natural processes and anthropogenic activities. Concurrently, climate change pressures, are likely to lead to severe ecological impacts on lagoon ecosystems. Among these, direct effects are mainly through changes in temperature and associated physico-chemical alterations, whereas indirect ones, mediated through processes such as extreme weather events in the catchment, include the alteration of nutrient loading patterns among others that can, in turn, modify the trophic states leading to depletion or to eutrophication. This phenomenon can lead, under certain circumstances, to harmful algal blooms events, anoxia, and mortality of aquatic flora and fauna, or to the reduction of primary production, with cascading effects on the whole trophic web with dramatic consequences for aquaculture, fishery, and recreational activities. The complexity of eutrophication processes, characterized by compounding and interconnected pressures, highlights the importance of adequate sophisticated methods to estimate future ecological impacts on fragile lagoon environments. In this context, a novel framework combining Machine Learning (ML) and biogeochemical models is proposed, leveraging the potential offered by both approaches to unravel and modelling environmental systems featured by compounding pressures. Multi-Layer Perceptron (MLP) and Random Forest (RF) models are used (trained, validated, and tested) within the Venice Lagoon case study to assimilate historical heterogenous WQ data (i.e., water temperature, salinity, and dissolved oxygen) and spatio-temporal information (i.e., monitoring station location and month), and to predict changes in chlorophyll-a (Chl-a) conditions. Then, projections from the biogeochemical model SHYFEM-BFM for 2049, and 2099 timeframes under RCP 8.5 are integrated to evaluate Chl-a variations under future bio-geochemical conditions forced by climate change projections. Annual and seasonal Chl-a predictions were performed out by classes based on two classification modes established on the descriptive statistics computed on baseline data: i) binary classification of Chl-a values under and over the median value, ii) multi-class classification defined by Chl-a quartiles. Results from the case study showed as the RF successfully classifies Chl-a under the baseline scenario with an overall model accuracy of about 80% for the median classification mode, and 61% for the quartile classification mode. Overall, a decreasing trend for the lowest Chl-a values (below the first quartile, i.e. 0.85 µg/l) can be observed, with an opposite rising fashion for the highest Chl-a values (above the fourth quartile, i.e. 2.78 µg/l). On the seasonal level, summer remains the season with the highest Chl-a values in all scenarios, although in 2099 a strong increase in Chl-a is also expected during the spring one. The proposed novel framework represents a valuable approach to strengthen both eutrophication modelling and scenarios analysis, by placing artificial intelligence-based models alongside biogeochemical models.

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.

Impact of Information and Communications Technologies (ICTs) on sustainable transport development is indisputable. It can be tested through various methods. The identification and detailed analysis of specific types of ICT solutions, case studies approach or scenarios analysis are examples of perspectives for impact assessment that can lead to the proof that successful applications of ICT solutions entails optimised, more accessible, less resource consuming and less emission intensive transport. The impact of ICTs in travel induction and substitution and resulting decarbonisation effect is difficult to assess isolated from other social, economic and technologic drivers. Financial applicability reflects initial investment cost as well as operation maintenance costs. Organizational feasibility refers primarily to the necessary changes in organisation and conduct of operations created by introduction of ICTs. The social factor results from user and public acceptance or lack of it. User acceptance is combination of many sub factors of which most important are: D2D travel time, D2D travel costs, comfort and convenience, safety and security. The objective of the article is to present the applicability of ICT solutions in passenger transport from the perspective of transport users taking into consideration real case studies from different European background. These case studies were identified as a result of specific selection process in order to receive a complex and differentiated sample. The first element of the selection process was the identification of ICT solutions to be applied in case studies. The next element was the choice of regions corresponding to varied economic and geographic characteristics. In addition, cultural factors have a strong impact on the behaviour of users. The resulting multidimensional factor matrix allows for the selection of best fitting case studies, which have potential to cover most of the ICT applications. Five European settings are selected for in-depth research. Each case represents different geographic, social and economic area, which forms a good European sample across differentiated setups. Case studies are supported by user surveys. Surveys allow for direct answers regarding user attitudes towards proposed ICTs. Based on this approach findings could be reported. The main conclusion is that users in various regions with very different characteristics as to the wealth, GDP levels, geography, and cultural backgrounds represent surprisingly similar attitudes towards ICTs. Specific findings regarding different types of solutions and different type of users are presented in the article. The analysis of selected case studies shows also that they could offer solution to many of transport sector problems including key issues from transport policy agenda: environment, mobility or accessibility. The paper is based on the authors research conducted within COMPASS project (Optimised co-modal passenger transport for reducing carbon emissions, project co-funded by the European Commission within the Seventh Framework Programme).

Operational risk is the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. It is difficult to model and measure operational risk of bank. The Bayesian network has a great deal of advantage in Modeling and measuring Operational Risk, which is induced by uncertain elements in a complicated system. After review operational risk of bank and Bayesian networks, this paper illustrates how to use Bayesian networks to manage operational risk by examples, include measure risk, casual analysis, and scenario Analysis etc. This paper also gives a Bayesian network model framework of operational risk.

Previous work has proposed the ontology-based semi-automatic generation of antipattern Bayesian Network(BN) models. The generated BN model can be used to illustrate the effects of uncertainty on antipatterns using Bayesian propagation. This can guide users in detecting particular antipattern attributes of importance based on uncertain ontological information. However, the proposed approach has been implemented in the Protege ontology editor environment and requires human intervention to specify how the BN model will be generated. The fully automated generation of ontology-based antipattern BN models still remains an open issue. SPARSE is an OWL ontology based intelligent system that assists software project managers in the antipattern detection process. In this paper, we propose the use of the resulting detected antipatterns of SPARSE, their attributes (i.e. causes, symptoms, consequences) and the ontological relationships between these attributes, in order to automatically generate BN models of the detected antipatterns. We illustrate how this approach can be implemented using an example of 8 antipattern attributes of 6 inter-related antipatterns detected using SPARSE.