Queensland University of Technology, Brisbane, Australia.
Mar Environ Res. 2010 Feb;69(1):27-37. doi: 10.1016/j.marenvres.2009.07.004. Epub 2009 Aug 3.
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.
束毛藻的水华现象在全球范围内已经存在了几十年。然而，随着这些水华的规模和频率的增加，加上藻类的毒性及其生物量的增加，它们已经成为重大的环境和健康问题。因此，必须更好地了解影响束毛藻水华发生的科学和管理因素。本文提出了一种综合贝叶斯网络（IBN）方法，该方法有助于整合各方对束毛藻的研究。该方法的关键是两个贝叶斯网络，分别对水华起始的管理和科学因素进行建模。研究发现，贝叶斯网络（BN），特别是面向对象的贝叶斯网络（OOBN）和动态 OOBN，有助于对关注的生态问题进行综合建模。通过 IBN 方法合并多个模型，可以探索问题的不同方面，这种方法适用于许多多方面的环境问题。
