Latent Variable Structured Bayesian Network for Cyanobacterial Risk Pre-control

P. Jiang, Xiao Liu, Jingjie Zhang, S. Te, K. Gin
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引用次数: 4

Abstract

Cyanobacterial blooms increasingly pose threats to ecosystems and human health. This paper is aimed to propose systematic risk pre-control schemes by understanding the complex causalities between cyanobacteria and multiple influencing variables. This research remains a challenge for three reasons. Firstly, the time-series evolution of cyanobacteria is characterized by deep uncertainties and nonlinear dynamics. Secondly, latent variables with hidden information usually exist in this kind of complex aquatic system. Thirdly, it is difficult to identify an efficient pre-control scheme that specifies variables for preferential regulation. To address these problems, we propose a latent variable structured Bayesian network model and a corresponding parameter learning algorithm. The model is tested by real-time spatio-temporal data. The computational results reveal that the proposed model demonstrates better performance in terms of inference accuracy and degree of system understanding. Based on sensitivity analysis and combination-effect analysis, a systematic risk pre-control scheme is proposed for decision-makers to prevent cyanobacterial blooms under the scenario of global warming.
蓝藻风险预控制的潜在变量结构贝叶斯网络
蓝藻大量繁殖对生态系统和人类健康的威胁越来越大。本文旨在通过了解蓝藻与多个影响变量之间的复杂因果关系,提出系统的风险预控方案。这项研究仍然是一个挑战,原因有三。首先,蓝藻的时间序列进化具有深度不确定性和非线性动力学特征。其次,在这类复杂的水生系统中,通常存在具有隐藏信息的潜在变量。第三,很难确定一种有效的预先控制方案,该方案为优惠调节指定变量。为了解决这些问题,我们提出了一个隐变量结构贝叶斯网络模型和相应的参数学习算法。利用实时时空数据对模型进行了验证。计算结果表明,该模型在推理精度和系统理解度方面都有较好的性能。基于敏感性分析和组合效应分析,提出了全球变暖情景下防范蓝藻华的系统性风险预控方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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