Algebraic approximation of event tree sequences

1984 ◽  
Vol 24 (3) ◽  
pp. 586
Author(s):  
Sam Hepenstal ◽  
Leishi Zhang ◽  
Neesha Kodogoda ◽  
B.L. William Wong

Criminal investigations are guided by repetitive and time-consuming information retrieval tasks, often with high risk and high consequence. If Artificial intelligence (AI) systems can automate lines of inquiry, it could reduce the burden on analysts and allow them to focus their efforts on analysis. However, there is a critical need for algorithmic transparency to address ethical concerns. In this paper, we use data gathered from Cognitive Task Analysis (CTA) interviews of criminal intelligence analysts and perform a novel analysis method to elicit question networks. We show how these networks form an event tree, where events are consolidated by capturing analyst intentions. The event tree is simplified with a Dynamic Chain Event Graph (DCEG) that provides a foundation for transparent autonomous investigations.


Author(s):  
Xinping Yan ◽  
Jinfen Zhang ◽  
Di Zhang ◽  
Carlos Guedes Soares

Concerns have been raised to navigational safety worldwide because of the increasing throughput and the passing ships during the past decades while maritime accidents such as collisions, groundings, overturns, oil-spills and fires have occurred, causing serious consequences. Formal Safety Assessment (FSA) has been acknowledged to be a framework widely used in maritime risk assessment. Under this framework, this paper discusses certain existing challenges when an effective safety assessment is carried out under a variety of uncertainties. Some theories and methodologies are proposed to overcome the present challenges, e.g., Fault/Event Tree Analysis (FTA/ETA), Evidential Reasoning (ER), Bayesian Belief Network (BBN) and Belief Rule Base (BRB). Subsequently, three typical case studies that have been carried out in the Yangtze River are introduced to illustrate the general application of those approaches. These examples aim to demonstrate how advanced methodologies can facilitate navigational risk assessment under high uncertainties.


2021 ◽  
Author(s):  
Beatriz Martínez Montesinos ◽  
Manuel Titos ◽  
Laura Sandri ◽  
Sara Barsotti ◽  
Giovanni Macedonio ◽  
...  

<p>Campi Flegrei is an active volcano located in one of the most densely inhabited areas in Europe and under high-traffic air routes. There, the Vesuvius Observatory’s surveillance system, which continuously monitors volcanic seismicity, soil deformations and gas emissions, highlights some variations in the state of the volcanic activity. It is well known that fragmented magma injected into the atmosphere during an explosive volcanic eruption poses a threat to human lives and air-traffic. For this reason, powerful tools and computational resources to generate extensive and high-resolution hazard maps taking into account a wide spectrum of events, including those of low probability but high impact, are important to provide decision makers with quality information to develop short- and long- term emergency plans. To this end, in the framework of the Center of Excellence for Exascale in Solid Earth (ChEESE), we show the potential of HPC in Probabilistic Volcanic Hazard Assessment. On the one hand, using the ChEESE's flagship Fall3D numerical code and taking advance of the PRACE-awarded resources at CEA/TGCC-HPC facility in France, we perform thousands of simulations of tephra deposition and airborne ash concentration at different flight levels exploring the natural variability and uncertainty on the eruptive conditions on a 3D-grid covering a 2 km-resolution 2000 km x 2000 km computational domain. On the other hand, we create short- and long-term workflows, by updating current Bayesian-Event-Tree-Analysis-based prototype tools, to make them capable of analyze the large amount of information generated by the Fall3D simulations that finally gives rise to the hazard maps for Campi Flegrei.</p>


2013 ◽  
Vol 13 (8) ◽  
pp. 1929-1943 ◽  
Author(s):  
M. Neri ◽  
G. Le Cozannet ◽  
P. Thierry ◽  
C. Bignami ◽  
J. Ruch

Abstract. Hazard mapping in poorly known volcanic areas is complex since much evidence of volcanic and non-volcanic hazards is often hidden by vegetation and alteration. In this paper, we propose a semi-quantitative method based on hazard event tree and multi-hazard map constructions developed in the frame of the FP7 MIAVITA project. We applied this method to the Kanlaon volcano (Philippines), which is characterized by poor geologic and historical records. We combine updated geological (long-term) and historical (short-term) data, building an event tree for the main types of hazardous events at Kanlaon and their potential frequencies. We then propose an updated multi-hazard map for Kanlaon, which may serve as a working base map in the case of future unrest. The obtained results extend the information already contained in previous volcanic hazard maps of Kanlaon, highlighting (i) an extensive, potentially active ~5 km long summit area striking north–south, (ii) new morphological features on the eastern flank of the volcano, prone to receiving volcanic products expanding from the summit, and (iii) important riverbeds that may potentially accumulate devastating mudflows. This preliminary study constitutes a basis that may help local civil defence authorities in making more informed land use planning decisions and in anticipating future risk/hazards at Kanlaon. This multi-hazard mapping method may also be applied to other poorly known active volcanoes.


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