Situation forecasting and decision-making optimization based on using markov finite chains for areas with industrial pollutions
DOI:
https://doi.org/10.32347/2410-2547.2020.104.164-174Keywords:
complex technical systems, engineering-within-nature complex systems, nature & engineering complex systems, environmental monitoring, emergency forecasting, statistical research, decision supportAbstract
The paper considers the issues of predicting the situations and optimizing decision-making to improve the environmental situations in the areas with industrial pollution based on the finite Markov’s chains.
The article systematizes the existing approaches to forecasting technological risks. The problems associated with the search for optimal forms of environmental safety management and approaches for predicting anthropogenic impact on the environment using mathematical models are considered. To predict the state of the environment, stochastic modeling is proposed, the basis of which is the theory of finite Markov chains. A technique for predicting and optimizing the economic effect on a discrete set of strategies has been developed. The figures show: building system states graph, determining the basic characteristics of system states, finding transition probabilities of Markov chains for non-critical states, a typical cycle of checking the model’s adequacy and system quality.
Based on the analysis of existing approaches to forecasting technological risks, a methodology has been developed for forecasting and optimizing the economic effect on a discrete set of strategies. The proposed methodology allows combining economic estimates with the ability to predict the situations and optimize decision-making to improve the environmental situation in the areas of possible chemical pollution.
Using the developed methodology will increase the efficiency of the industrial enterprises, facilitate generating informed management decisions, create software and hardware ways to respond the emergencies.
The methodology for modeling engineering within nature complex systems and the optimization of decision-making based on finite Markov chains in the areas with industrial pollution will be helpful to researchers and operators of complex technical systems in predicting emergencies using environmental monitoring systems.
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