A Bayesian network model for integrative river rehabilitation planning and management

Artificial intelligence to explain the variables that favor the cyanobacteria steady-state in tropical ecosystems: A Bayeasian network approach

The steady-state is a situation of little variability of species dominance and total biomass over time. Maintenance of cyanobacteria are often observed in tropical and eutrophic ecosystems and can cause imbalances in aquatic ecosystem. Bayeasian networks allow the construction of simpls models that summarizes a large amount of variables and can predict the probability of occurrence of a given event. Studies considering Bayeasian networks built from environmental data to predict the occurrence of steady-state in aquatic ecosystems are scarce. This study aims to propose a Bayeasian network model to assess the occurrence, composition and duration of cyanobacteria steady-state in a tropical and eutrophic ecosystem. It was hypothesized long lasting steady-state events, composed by filamentous cyanobacteria species and directly influenced by eutrophication and drought. Our model showed steady-state lasting between 3 and 17 weeks with the monodominance or co-dominance of filamentous species, mainly Raphidiopsis raciborskii and Planktothrix agardhii. These evens occurred frequently under drought and high turbidity, however higher nutrients concentrations did not increase the probability steady-state occurrence or longer duration. The proposed model appears as a tool to assess the effects of future warming on steady-state occurrence and it can be a useful to more traditional process-based models for reservoirs.

Increased Use of Bayesian Network Models Has Improved Environmental Risk Assessments

Environmental and ecological risk assessments are defined as the process for evaluating the likelihood that the environment may be impacted as a result of exposure to stressors. Although this definition implies the calculation of probabilities, risk assessments traditionally rely on nonprobabilistic methods such as calculation of a risk quotient. Bayesian network (BN) models are a tool for probabilistic and causal modeling, increasingly used in many fields of environmental science. Bayesian networks are defined as directed acyclic graphs where the causal relationships and the associated uncertainty are quantified in conditional probability tables. Bayesian networks inherently incorporate uncertainty and can integrate a variety of information types, including expert elicitation. During the last 2 decades, there has been a steady increase in reports on BN applications in environmental risk assessment and management. At recent annual meetings of the Society of Environmental Toxicology and Chemistry (SETAC) North America and SETAC Europe, a number of applications of BN models were presented along with new theoretical developments. Likewise, recent meetings of the European Geosciences Union (EGU) have dedicated sessions to Bayesian modeling in relation to water quality. This special series contains 10 articles based on presentations in these sessions, reflecting a range of BN applications to systems, ranging from cells and populations to watersheds and national scale. The articles report on recent progress in many topics, including climate and management scenarios, ecological and socioeconomic endpoints, machine learning, diagnostic inference, and model evaluation. They demonstrate that BNs can be adapted to established conceptual frameworks used to support environmental risk assessments, such as adverse outcome pathways and the relative risk model. The contributions from EGU demonstrate recent advancements in areas such as spatial (geographic information system [GIS]-based) and temporal (dynamic) BN modeling. In conclusion, this special series supports the prediction that increased use of Bayesian network models will improve environmental risk assessments. Integr Environ Assess Manag 2021;17:53-61. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes.

Quantifying rehabilitation risks for surface-strip coal mines using a soil compaction Bayesian network in South Africa and Australia: To demonstrate the R2 AIN Framework

Environmental information is acquired and assessed during the environmental impact assessment process for surface-strip coal mine approval. However, integrating these data and quantifying rehabilitation risk using a holistic multidisciplinary approach is seldom undertaken. We present a rehabilitation risk assessment integrated network (R² AIN™) framework that can be applied using Bayesian networks (BNs) to integrate and quantify such rehabilitation risks. Our framework has 7 steps, including key integration of rehabilitation risk sources and the quantification of undesired rehabilitation risk events to the final application of mitigation. We demonstrate the framework using a soil compaction BN case study in the Witbank Coalfield, South Africa and the Bowen Basin, Australia. Our approach allows for a probabilistic assessment of rehabilitation risk associated with multidisciplines to be integrated and quantified. Using this method, a site's rehabilitation risk profile can be determined before mining activities commence and the effects of manipulating management actions during later mine phases to reduce risk can be gauged, to aid decision making. Integr Environ Assess Manag 2019;15:190-208. © 2019 SETAC.

A Bayesian network for assessing the collision induced risk of an oil accident in the Gulf of Finland

The growth of maritime oil transportation in the Gulf of Finland (GoF), North-Eastern Baltic Sea, increases environmental risks by increasing the probability of oil accidents. By integrating the work of a multidisciplinary research team and information from several sources, we have developed a probabilistic risk assessment application that considers the likely future development of maritime traffic and oil transportation in the area and the resulting risk of environmental pollution. This metamodel is used to compare the effects of two preventative management actions on the tanker collision probabilities and the consequent risk. The resulting risk is evaluated from four different perspectives. Bayesian networks enable large amounts of information about causalities to be integrated and utilized in probabilistic inference. Compared with the baseline period of 2007-2008, the worst-case scenario is that the risk level increases 4-fold by the year 2015. The management measures are evaluated and found to decrease the risk by 4-13%, but the utility gained by their joint implementation would be less than the sum of their independent effects. In addition to the results concerning the varying risk levels, the application provides interesting information about the relationships between the different elements of the system.

Integrated modelling for Sustainability Appraisal of urban river corridors: going beyond compartmentalised thinking

Sustainability Appraisal (SA) is a complex task that involves integration of social, environmental and economic considerations and often requires trade-offs between multiple stakeholders that may not easily be brought to consensus. Classical SA, often compartmentalised in the rigid boundary of disciplines, can facilitate discussion, but can only partially inform decision makers as many important aspects of sustainability remain abstract and not interlinked. A fully integrated model can overcome compartmentality in the assessment process and provides opportunity for a better integrative exploratory planning process. The objective of this paper is to explore the benefit of an integrated modelling approach to SA and how a structured integrated model can be used to provide a coherent, consistent and deliberative platform to assess policy or planning proposals. The paper discusses a participative and integrative modelling approach to urban river corridor development, incorporating the principal of sustainability. The paper uses a case study site in Sheffield, UK, with three alternative development scenarios, incorporating a number of possible riverside design features. An integrated SA model is used to develop better design by optimising different design elements and delivering a more sustainable (re)-development plan. We conclude that participatory integrated modelling has strong potential for supporting the SA processes. A high degree of integration provides the opportunity for more inclusive and informed decision-making regarding issues of urban development. It also provides the opportunity to reflect on their long-term dynamics, and to gain insights on the interrelationships underlying persistent sustainability problems. Thus the ability to address economic, social and environmental interdependencies within policies, plans, and legislations is enhanced.

Fully connected Bayesian belief networks: a modeling procedure with a case study of the Ganges river basin

The use of Bayesian Belief Networks (BBNs) in modeling of environmental and natural resources systems has gradually grown, and they have become one of the mainstream approaches in the field. They are typically used in modeling complex systems in which policy or management decisions must be made under high uncertainties. This article documents an approach to constructing large and highly complex BBNs using a matrix representation of the model structure. This approach allows smooth construction of highly complicated models with intricate likelihood structures. A case study of the Ganges river basin, the most populated river basin of the planet, is presented. Four different development scenarios were investigated with the purpose of reaching the Millennium Development Goals and Integrated Water Resources Management goals, both promoted by the United Nations Agencies. The model results warned against the promotion of economic development policies that do not place strong emphasis on social and environmental concerns.

Bayesian networks in environmental and resource management

This overview article for the special series, "Bayesian Networks in Environmental and Resource Management," reviews 7 case study articles with the aim to compare Bayesian network (BN) applications to different environmental and resource management problems from around the world. The article discusses advances in the last decade in the use of BNs as applied to environmental and resource management. We highlight progress in computational methods, best-practices for model design and model communication. We review several research challenges to the use of BNs in environmental and resource management that we think may find a solution in the near future with further research attention.