Informing the management of multiple stressors on estuarine ecosystems using an expert-based Bayesian Network model

Failure Risk Assessment of Coal Gasifier Based on the Integration of Bayesian Network and Trapezoidal Intuitionistic Fuzzy Number-Based Similarity Aggregation Method (TpIFN-SAM)

The coal gasifier is the core unit of the coal gasification system. Due to its exposure to high temperatures, high pressures, and aggressive media, it is highly susceptible to serious accidents in the event of failure. Therefore, it is important for the gasifier to perform failure-risk assessment to understand its safety status and provide safety measures. Bayesian networks (BNs) for risk analysis of process systems has received a lot of attention due to its powerful inference capability and its ability to reflect complex relationships between risk factors. However, the acquisition of basic probability data in a Bayesian network is always a great challenge. In this study, an improved Bayesian network integrated with a trapezoidal intuitionistic fuzzy number-based similarity aggregation method (TpIFN–SAM) is proposed for the failure-risk assessment of process systems. This approach used the TpIFN–SAM to collect and aggregate experts’ opinions for obtaining the prior probabilities of the root events in the BN. In the TpIFN–SAM, the intuitionistic fuzzy analytic-hierarchy-process method (IF-AHP) was adopted to assign the expert weights for reducing subjectivity or the bias caused by individual differences. To clarify the suitability of the proposed method, a case study of a coal gasifier was demonstrated, and both prediction and diagnosis analyses of the BN were performed; finally, the weak links of the gasifier were identified.

A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management

Models of ecological response to multiple stressors and of the consequences for ecosystem services (ES) delivery are scarce. This paper describes a methodology for constructing a BBN combining catchment and water quality model output, data, and expert knowledge that can support the integration of ES into water resources management. It proposes "small group" workshop methods for elucidating expert knowledge and analyses the areas of agreement and disagreement between experts. The model was developed for four selected ES and for assessing the consequences of management options relating to no-change, riparian management, and decreasing or increasing livestock numbers. Compared with no-change, riparian management and a decrease in livestock numbers improved the ES investigated to varying degrees. Sensitivity analysis of the expert information in the BBN showed the greatest disagreements between experts were mainly for low probability situations and thus had little impact on the results. Conversely, in our applications, the best agreement between experts tended to occur for the higher probability, more likely, situations. This has implications for the practical use of this type of model to support catchment management decisions. The complexity of the relationship between management measures, the water quality and ecological responses and resulting changes in ES must not be a barrier to making decisions in the present time. The interactions of multiple stressors further complicate the situation. However, management decisions typically relate to the overall character of solutions and not their detailed design, which can follow once the nature of the solution has been chosen, for example livestock management or riparian measures or both.

Effects of change in streamflow patterns on water quality

Streamflow patterns are closely linked with the quality of stream water, but they are often dealt separately. Due to this, the effects of change in streamflow patterns resulting from river regulation and flow diversion on stream water quality remain under-investigated. This study models change in water quality indicators including pollutants (total suspended solids and turbidity), nutrients (total nitrogen and phosphorus), dissolved oxygen, nitrogen (kjeldahl), pH, and salinity caused by the change in streamflow patterns under different scenarios of river regulation, flow diversion, and rainfall. The generalized additive model was used and the Goulburn-Broken catchment, Australia was chosen as the case study. It was found that concentrations of pollutants and nutrients increased by 38% while dissolved oxygen and nitrogen (kjeldahl) decreased by 35% during the period 1990-2018. These changes were associated with an average increase of 20% in low and medium flows, an average decline of 22% in high and overbank flows and a 15% decline in rainfall. Under the scenario of climate change, river regulation and flow diversion, the overbank flow patterns would mimic the effects of low and medium flows on the water quality indicators that would raise the concentration of pollutants, nutrients, and salinity by 19%. Restoration of high flows would decrease these concentrations by 28% relative to current concentrations, however, it would also reduce dissolved oxygen, nitrogen (kjeldahl), and pH. Effects of streamflow patterns on water quality have implications for environmental flow management, thus, this study recommends critical adjustments in low, medium, and high flows for improving water quality.