Santorini unrest 2011–2012: an immediate Bayesian belief network analysis of eruption scenario probabilities for urgent decision support under uncertainty
Santorini unrest 2011–2012: an immediate Bayesian belief network analysis of eruption scenario probabilities for urgent decision support under uncertainty
复制标题
2011-2012 年圣托里尼岛动乱：对喷发场景概率进行即时贝叶斯信念网络分析，以支持不确定性下的紧急决策
作者：
W. Aspinall;G. Woo
2011-2012年希腊圣托里尼火山岛的动荡是一些政府不安的原因，他们担心如果火山爆发，他们的国民在这个受欢迎的度假岛屿上会面临风险。为了支持英国政府进行的紧急响应计划，我们开发了一个快速评估不同的喷发情景概率，使用贝叶斯信念网络（BBN）的制定相结合的多股科学和观测证据。在这里，我们提出了2012年初设计的三种替代BBN模型，用于评估局势：（1）一个基本静态网络，用于评估任何一个时刻的概率，仅使用四个关键的动荡指标;（2）一个复合时间步进网络，扩展基本网络，随着指标的变化更新概率;（3）一个更全面的网络，包括多条线的其他数据和观测，反映了现代多参数监测技术的多样性。一个关键的结论是，即使只有三个或四个基本指标，试图从心理上判断动荡迹象的含义是不可行的，也是站不住脚的--使用贝叶斯规则的结构化概率程序是可靠地列举证据强度的理性方法。在圣托里尼事件中，骚乱和官方的焦虑很快就消失了，我们的方法没有进展到详细考虑BBN参数、分析数据不确定性或引发专家判断以量化BBN中使用的不确定性的程度。如果这样做了，由此产生的情景概率就可以用来确定火山灾害的可能性和可能的喷发活动造成的风险，正如对潜在火山影响的规模和强度的同时评估所确定的那样（Jenkins et.例如，希腊圣托里尼火山未来喷发的火山灰和气体危险性评估。即将出版）。理想的情况是，这种危害和风险评估应该在危机级别的动荡发展之前就详细阐述和批评，而不是在局势看起来不妙的几个小时内启动和实施。特别是，需要对所有信息进行仔细分析，以全面和可靠地确定和表示参数不确定性。
Unrest at the Greek volcanic island of Santorini in 2011–2012 was a cause for unease for some governments, concerned about risks to their nationals on this popular holiday island if an eruption took place. In support of urgent response planning undertaken by the UK government, we developed a rapid evaluation of different eruption scenario probabilities, using the Bayesian Belief Network (BBN) formulation for combining multiple strands of scientific and observational evidence. Here we present three alternative BBN models that were devised in early 2012 for assessing the situation: (1) a basic static net for evaluating probabilities at any one moment in time, utilising just four key unrest indicators; (2) a compound time-stepping net, extending the basic net to update probabilities through time as the indicators changed; and (3) a more comprehensive net, with multiple lines of other data and observations incorporated, reflecting diversity of modern multi-parameter monitoring techniques. A key conclusion is that, even with just three or four basic indicators, it is not feasible, or defensible, to attempt to judge mentally the implications of signs of unrest – a structured probabilistic procedure using Bayes’ Rule is a rational approach for enumerating evidential strengths reliably. In the Santorini case, the unrest, and official anxiety, diminished quite quickly and our approach was not progressed to the point where detailed consideration was given to BBN parameters, analysis of data uncertainty or the elicitation of expert judgements for quantifying uncertainties to be used in the BBN. Had this been done, the resulting scenario probabilities could have been adopted to determine likelihoods of volcanic hazards and risks caused by possible eruptive activity, as identified in a concurrent assessment of the scale and intensities of potential volcanic impacts (Jenkins et. al., Assessment of ash and gas hazard for future eruptions at Santorini Volcano, Greece. forthcoming). Ideally, such hazard and risk assessments should be elaborated in detail and critiqued well before crisis-level unrest develops – not initiated and implemented within a few hours just when a situation looks ominous. In particular, careful analysis of all information is required to determine and represent parameter uncertainties comprehensively and dependably.
影响因子：
18.3
作者：
M. Parks;J. Biggs;P. England;T. Mather;P. Nomikou;Kirill Palamartchouk;X. Papanikolaou;D. Paradissis;B. Parsons;D. Pyle;Costas Raptakis;V. Zacharis
通讯作者：
M. Parks;J. Biggs;P. England;T. Mather;P. Nomikou;Kirill Palamartchouk;X. Papanikolaou;D. Paradissis;B. Parsons;D. Pyle;Costas Raptakis;V. Zacharis