Scenario analysis of management processes in the prevention and the elimination of consequences of man-made disasters

Kulba, Vladimir; Bakhtadze, Natalia; Zaikin, Oleg; Shelkov, Alexey B.; Chernov, Igor

doi:10.1016/j.procs.2017.08.247

Cited by 13 publications

(4 citation statements)

References 2 publications

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“…For example, under an accident state of T 1 , if a firefighting attack instead of emergency avoidance is carried out, huge losses could be caused. Table 4 shows the loss probabilities of different schemes corresponding to different accident states developed based on historical data [22][23][24]. (3) A first round of decision-making is initiated, and decision-making losses of those four schemes of P 1 , P 2 , P 3 , and P 4 corresponding to four accident states of T 1 , T 2 , T 3 , and T 4 are calculated, respectively.…”

Section: Emergency Decision-making Processes Of Major Accidentsmentioning

confidence: 99%

Research on Sequential Decision-Making of Major Accidents with Incomplete Information

Xia,

Chen,

Zheng

et al. 2024

Fire

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In order to solve the problem of emergency decision-making with incomplete information and deal with the accident information in different time series at the scenes of major accidents, this paper proposes a method of sequential decision-making by utilizing the relevant knowledge of D-S evidence theory and game theory. Firstly, we took an oil tank fire accident as an example and sorted out historical cases and expert experiences to establish a logical relationship between key accident scenes and accident scene symptoms in the accident. Meanwhile, we applied the logistic regression analysis method to obtain the basic probability distribution of each key accident scene in the oil tank fire, and on this basis, we constructed an evidence set of the fire. Secondly, based on the D-S evidence theory, we effectively quantified the knowledge uncertainty and evidence uncertainty, with the incomplete and insufficient information taken as an evidence system of the development of key accident scenes to construct a situation prediction model of these accident scenes. Thirdly, based on the game theory, we viewed emergency decision-makers and major accidents as two sides of the game to compare and analyze accident states at different time points and solve the contradiction between loss costs of decision-making and information collection costs. Therefore, this paper has provided a solution for the optimization of accident schemes at different time stages, thus realizing the sequential decision-making at the scenes of major accidents. Furthermore, we combined the situation prediction model with sequential decision-making, with the basic steps described below: (1) We drew up an initial action plan in the case of an extreme lack of information; then, we (2) started to address the accident and constructed a framework of accident identification, (3) collected and dealt with the continuously added evidence information with the evolution of the accident, (4) calculated the confidence levels of key accident scenarios after evaluating different evidence and then predicted the accident state in the next stage, and (5) calculated the profit–loss ratio between the current decision-making scheme and the decision-making scheme of the next stage. Finally, we (6) repeated steps (3) to (5) until the accident completely vanished. We verified the feasibility of the proposed method with the explosion accident of the Zhangzhou P.X. project in Fujian on 6 April used as an example. Based on the D-S evidence theory, this method employs approximate reasoning on the incomplete and insufficient information obtained at the scenes of major accidents, thus realizing the situation prediction of key scenes of these accidents. Additionally, this method uses the game theory to solve the contradiction between decision-making loss costs and information collection costs, thus optimizing the decision-making schemes at different time stages of major accidents.

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Section: Emergency Decision-making Processes Of Major Accidentsmentioning

confidence: 99%

Research on Sequential Decision-Making of Major Accidents with Incomplete Information

Xia,

Chen,

Zheng

et al. 2024

Fire

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“…The conclusion about the structural stability of the system on the simulation model is made after the analysis of the cycles of the graph-the cognitive model. A criterion of structural stability is used in the analysis of cognitive maps [2,6,30,36]; a system model is considered structurally stable if it contains an odd number of negative (stabilizing) feedback loops. Figures 7 and 8 show the analysis of the cognitive model G results cycles.…”

Section: Analysis Of Paths Cycles and Structural Stabilitymentioning

confidence: 99%

Simulation Cognitive Modeling Approach to the Regional Sustainable Complex System Development for Improving Quality of Life

Firsova,

Gorelova,

Makarova

et al. 2023

Mathematics

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This article presents study results of the region’s sustainable development possibility, thus improving the population’s life quality using cognitive simulation methods of complex systems. The main theoretical provisions of cognitive modeling developed and tested earlier in various socioeconomic system modeling are briefly outlined. The cognitive modeling application’s mathematical apparatus and CMCS software No. 2018661506 system were developed using quantitative data from one of Russia’s southern regions (Rostov oblast). The task was to study the region, model, understand, explain, and develop possible situation development scenarios, and foresee this complex system’s possible future outcome. The main statistical socioeconomic indicators of the state region were studied and processed. Data analysis necessary for developing and researching a cognitive model is given. The regional economic mechanism cognitive model is a functional graph consisting of quantitative and qualitative concepts. Between them, relationships are given in the form of functions, which is the novelty of research. The results of several scenarios of impulse modeling are presented, making it possible to predict future desirable and undesirable processes in the system. Scenario analysis was carried out, making it possible to propose a number of recommendations for the region’s sustainable development. A direction for the region’s development of further cognitive research is proposed.

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“…In contrast to experiments conducted in physics, where the possibility of their multiple conduct, the conditions of the functioning of information systems are characterized by a constant impact of negative external influences and are constantly changing [4], and consequently the repetition of the experiment under the same conditions is practically impracticable. The laws of probability distribution of risk events, as a rule, do not correspond to the law of the normal Gaussian distribution [5,6].…”

Section: Introductionmentioning

confidence: 99%

Risk evaluation in information systems using continuous and discrete distribution laws

Singh¹,

Prakash²

2020

Eng. Appl. Sci. Lett.

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The paper construct continuous and discrete distribution laws, used to assess risks in information systems. Generalized expressions for continuous distribution laws with maximum entropy are obtained. It is shown that, in the general case, the entropy also depends on the type of moments used to determine the numerical characteristics of the distribution law. Also, probabilistic model have been developed to analyze the sequence of independent trials with three outcomes. Expressions for their basic numerical characteristics are obtained, as well as for calculating the probabilities of occurrence of the corresponding events.

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Scenario analysis of management processes in the prevention and the elimination of consequences of man-made disasters

Cited by 13 publications

References 2 publications

Research on Sequential Decision-Making of Major Accidents with Incomplete Information

Research on Sequential Decision-Making of Major Accidents with Incomplete Information

Simulation Cognitive Modeling Approach to the Regional Sustainable Complex System Development for Improving Quality of Life

Risk evaluation in information systems using continuous and discrete distribution laws

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