An Integrated Bayesian Network approach to Lyngbya majuscula bloom initiation

Abstract

Blooms of the cyanobacteria Lyngbya majuscula have occurred for decades around the world. However, with the increase in size and frequency of these blooms, coupled with the toxicity of such algae and their increased biomass, they have become substantial environmental and health issues. It is therefore imperative to develop a better understanding of the scientific and management factors impacting on Lyngbya bloom initiation. This paper suggests an Integrated Bayesian Network (IBN) approach that facilitates the merger of the research being conducted by various parties on Lyngbya. Pivotal to this approach are two Bayesian networks modelling the management and scientific factors of bloom initiation. The research found that Bayesian Networks (BN) and specifically Object Oriented BNs (OOBN) and Dynamic OOBNs facilitate an integrated approach to modelling ecological issues of concern. The merger of multiple models which explore different aspects of the problem through an IBN approach can apply to many multi-faceted environmental problems.

Preliminary Study on Bridge Health Prediction Using Dynamic Objective Oriented Bayesian Network (DOOBN)

“…As an extension of conventional BN, an Object Oriented Bayesian Network (OOBN) contains, in addition to the usual nodes, instance nodes [7].The basic construction block in an OOBN is an object, which can be a physical or an abstract entity, or a relationship between two entities [8]. The object represents either a node or an instantiation of a network class (instance nodes).…”

Modeling the viability of the free‐ranging cheetah population in Namibia: an object‐oriented Bayesian network approach

“…Instead, all perspectives need to be considered and integrated to form a cohesive model for use in planning strategic conservation actions. Bayesian networks (BNs) are ideally suited for interactive, integrated modeling (Johnson et al 2010a). A BN is a mathematical model (Pearl 1988, Neapolitan 1990, Jensen and Nielsen 2007 providing a graphical representation of key factors and interactions for an outcome of interest (Jensen and Nielsen 2007, Uusitalo 2007, Johnson et al 2010a such as the viability of a freeranging cheetah (Acinonyx jubatus) population.…”

“…Bayesian networks (BNs) are ideally suited for interactive, integrated modeling (Johnson et al 2010a). A BN is a mathematical model (Pearl 1988, Neapolitan 1990, Jensen and Nielsen 2007 providing a graphical representation of key factors and interactions for an outcome of interest (Jensen and Nielsen 2007, Uusitalo 2007, Johnson et al 2010a such as the viability of a freeranging cheetah (Acinonyx jubatus) population. Key factors are represented as nodes in the diagram and their dependencies on other key factors and the outcome of interest (target node) are depicted as directed links to form a directed acyclic graph (Lauritzen andSheehan 2003, Johnson et al 2010b).…”

“…Objectoriented Bayesian networks (OOBN) are BNs which have interface nodes (input nodes and output nodes) and instance nodes (representing another network or network fragment). Interface nodes enable connectivity with other OOBNs by providing the flow of information into (input node) and out of (output node) the BN from, or to, other OOBNs (Hugin 2007, Jensen and Nielsen 2007, Johnson et al 2010a). This enables the construction of complex and dynamic models (Koller andPfeffer 1997, Johnson et al 2010a) when traditional BNs are often inadequate (Uusitalo 2007, Johnson and.…”

“…The application of OOBN modeling techniques with its parallel concurrent development of inter-related, yet self-contained networks by specialist expert teams (Johnson et al 2010a) is eminently transferrable to modeling the issues facing conservation and management of other species of interest or concern.…”