An application of Bayesian Belief Networks to assess management scenarios for aquaculture in a complex tropical lake system in Indonesia

Bayesian belief networks (BBN) and fuzzy cognitive maps (FCM) are two major causal knowledge frameworks that are frequently used in various domains for cause and effect analysis. However, most researchers use these as separate approaches to analyse the cause(s) and effect(s) of an event. In practice, both methods have their own strengths and weaknesses in both causal modelling and causal analysis. In this paper, a combination of BBN and FCM is used in order to model and analyse network intrusions. First, the BBN is learnt from network intrusion data; following this, an FCM is generated from the BBN, using a migration method. A data-mining approach is suitable for use in the construction of a BBN for network intrusion since this is a data-rich domain, while an FCM is appropriate for the intuitive representation of complex domains. The proposed method of network intrusion analysis using both BBN and FCM consists of several stages, in order to leverage the capabilities of each approach in building the causal model and performing causal analysis. Both the intuitive representation of the causal model in FCM and the wide variety of reasoning methods supported by BBN are exploited in this research to facilitate network intrusion analysis.

Bayesian Belief Networks (BBN) modeling the water quality has become popular due to advances in computational techniques. For this instance, BBN is a useful tool to modeling the relationship between water quality data and population or urbanization parameters on a watershed scale. This method can combine primary water quality data and decision parameters and help scientists and decision-makers analyze several scenarios on a watershed, including the effect of scale. This paper aims to analyze and discuss the application of Bayesian Belief Network (BBN) on the relationship between watershed water quality and sanitary management indicators, studying a case on the Pantanal Wetland tributary watershed. Two scales BBN were constructed using ten years of water quality and sewage management datasets. Both BBNs were responsive and sensitive to water quality parameters. The Total Nitrogen and E. coli were de most essential parameters to simulate changes in water quality scenarios. The simulated scenarios showed structural limitations about the Pantanal Wetland Cities' sanitary system in the present study. We strongly recommend a review of the goals of sanitary structure and services and alert to the risk of a sanitary crisis in Pantanal Wetland.

The problem of the safe interaction between a Nuclear Power Plant (NPP) and a Power Grid (PG), considering the Fukushima nuclear accident, is becoming topical. There are a lot different types of influences between NPPs and PG, which stipulate NPPs' safety levels. To evaluate the influences, two metrics are proposed: linguistic and numerical. The approach to the NPP-PG safety assessment is based on the application of Bayesian Belief Network (BBN), where nodes represent different PG systems and links are stipulated by different types of influences (physical, informational, geographic, etc). It is suggested to evaluate criticality of the PG system considering the change of criticalities of all connected systems. The total criticality of each node in BBN is assessed considering particular criticalities caused by different types of influence. The complex nature of NPP and PG mutual interaction calls for the need for integration of different methods that use input data of different qualimetric nature (deterministic, stochastic, linguistic). Application of one specified group of risk methods might lead to loss and/or disregard of a part of safety-related information. BBN and Fuzzy Logic (FL) represent a basis for development of the hybrid approach to capture all information required for safety assessment of NPP – PG under uncertainties. Integration of FL-based methods and BBNs allows decreasing the amount of input information (measurements) required for safety assessment, when these methods are used independently outside from the proposed integration framework. An illustrative example for the NPP reactor safety assessment is considered in this chapter.

Application of Bayesian Belief Networks to quantify and map areas at risk to soil threats: Using soil compaction as an example

The problem of the safe interaction between a Nuclear Power Plant (NPP) and a Power Grid (PG), considering the Fukushima nuclear accident, is becoming topical. There are a lot different types of influences between NPPs and PG, which stipulate NPPs’ safety levels. To evaluate the influences, two metrics are proposed: linguistic and numerical. The approach to the NPP-PG safety assessment is based on the application of Bayesian Belief Network (BBN), where nodes represent different PG systems and links are stipulated by different types of influences (physical, informational, geographic, etc). It is suggested to evaluate criticality of the PG system considering the change of criticalities of all connected systems. The total criticality of each node in BBN is assessed considering particular criticalities caused by different types of influence. The complex nature of NPP and PG mutual interaction calls for the need for integration of different methods that use input data of different qualimetric nature (deterministic, stochastic, linguistic). Application of one specified group of risk methods might lead to loss and/or disregard of a part of safety-related information. BBN and Fuzzy Logic (FL) represent a basis for development of the hybrid approach to capture all information required for safety assessment of NPP – PG under uncertainties. Integration of FL-based methods and BBNs allows decreasing the amount of input information (measurements) required for safety assessment, when these methods are used independently outside from the proposed integration framework. An illustrative example for the NPP reactor safety assessment is considered in this chapter.

Bayesian Belief Networks as a tool for evidence-based conservation management

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.

The application of Bayesian belief networks in the analysis of reliability is considered. An algorithm for constructing a mathematical model in Bayesian belief networks is developed. An example of reliability analysis of a radioelectronic system by using Bayesian belief networks is given. It is shown that the developed model of Bayesian networks allows estimating the probability of failure-free operation, identifying possible failures and modeling failure states. Keywords Bayesian networks, reliability, reliability analysis, electronic system, fault tree analysis

Fires become one of the common challenges faced by smart cities. As one of the most efficient ways in the safety science field, risk assessment could determine the risk in a quantitative or qualitative way and recognize the threat. And Bayesian Belief Networks (BBNs) has gained a reputation for being powerful techniques for modeling complex systems where the variables are highly interlinked and have been widely used for quantitative risk assessment in different fields in recent years. This work is aimed at further exploring the application of Bayesian Belief Networks for smart city fire risk assessment using history statistics and sensor data. The dynamic urban fire risk assessment method, Bayesian Belief Networks (BBNs), is described. Besides, fire risk associated factors are identified, thus a BBN model is constructed. Then a case study is presented to expound the calculation model. Both the results and discussion are given.

Designers of dependable systems need to present assurance cases that support the claims made about the system’s dependability. Building this assurance case, incorporating different types of evidence and reasoning, can be daunting. In this paper we argue that, thanks to their flexibility and expressive capabilities, Bayesian Belief Networks are particularly suitable for building such assurance cases. Drawing on our experience preparing and presenting an assurance case to certify a software product to IEC 61508 Safety Integrity Level 3, we describe how Bayesian Belief Networks can be used to simplify both the engineer’s work in preparing the case, and the auditor’s or customer’s work in checking this case for coherence and completeness

Bayesian Belief Networks: Redefining wholesale electricity price modelling in high penetration non-firm renewable generation power systems

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 (FC) in a hybrid system based on 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 are generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks (BBNs) 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 a 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.

The combinations of low probability events (hardware and software faults, anomalous nature events, human operator errors) cause the infrastructure accidents and disruptions. There are different approaches for evaluation of human operator's reliability. Multi-factor analysis is the essential step for obtaining the trustworthiness estimations of infrastructure's safety. The application of Bayesian Belief Networks (BBN) as a basis of multi-factor safety analysis is suggested in the paper. Two approaches for integration of probabilistic estimations in different qualimetric scales are proposed. The example of using of BBN for assessment of human factor in NPP Fukushima-1 disaster is considered.

Many techniques are used for cost, quality and schedule estimation in the context of software risk management. Application of Bayesian Belief Networks (BBN) in this area permits process metrics and product metrics (static code metrics) to be considered in a causal way (i.e. each variable within the model has a cause-effect relationship with other variables) and, in addition, current observations can be used to update estimates based on historical data. However, the real situation that researchers face is that process data is often inadequately, or inappropriately, collected and organized by the development organization. In this paper, we explore if BBN could be used to predict appropriate release dates for a new set of products from a telecommunication company based on static code metrics data and limited process information collected from a earlier set of the same products. Two models are evaluated with different methods involved to analyze the available metrics data.

As differing evaluation instruments are adopted in learning object repositories serving specialized communities of users, what methods can be adopted for translating evaluative data across instruments in order to share this data among different repositories? How can evaluation from different reviewers be properly integrated? How can explicit and implicit measures of preference and quality be combined to recommend objects to users? In this research we studied the application of Bayesian belief network (BBN) to the problem of insufficient and incomplete reviews during learning objects evaluation, and translating and integrating data among different quality evaluation instruments and measures. Two BBNs were constructed to probabilistically model relationships among different roles of reviewers as well as among items of different evaluation measurements. Initial testing using hypothetic data showed that the model was able to make potentially useful inferences about different dimensions of learning object quality. We further extend our model over geographic distances assuming that the reviewers would be distributed and that each reviewer would change the underlying BBN network (to a certain extent) to suit his/her expertise. We highlight issues that arise due to a highly distributed and personalized BBN network that can be used to make valid inferences about learning object quality.