Model of monitoring of oil soil pollution and its termination
- Authors: Germanova S.E.1, Magdeeva T.V.1, Pliushchikov V.G.1
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Affiliations:
- Рeoples’ Friendship University of Russia
- Issue: Vol 16, No 2 (2021)
- Pages: 146-153
- Section: Agricultural technologies and land reclamation
- URL: https://journal-vniispk.ru/2312-797X/article/view/315457
- DOI: https://doi.org/10.22363/2312-797X-2021-16-2-146-153
- ID: 315457
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Abstract
The assessment of impact of oil production economic activities on land pollution in Russia contributes to evolutionary management decision making. Oil industrial pollution affects negatively flora and fauna. Thus, it’s important to identify the level of its exposure and danger, the site of contamination. A system approach is needed. When studying the environment, it’s necessary to consider the presence of risk situations and stochastic irreversible changes. It’s essential to identify the nature and type of soil contamination with petroleum products using high-tech tools, intellectual procedures. The work considers modeling of such situation, forecasting and identification of oil contaminants. The submodel of optimal termination of monitoring is also considered. Ending monitoring of environmental optimization will result in lower monitoring costs, since monitoring oilcontaminated environments is an expensive and complex technological mechanism, often requiring satellite data. The proposed algorithm for modeling and system analysis is based on situational modeling. Evolutionary modeling allows to adapt the procedure (methodology) of forecasting and assessment to environmental risk factors. It increases the accuracy (formalization and evidence) and completeness of conclusions, the efficiency of situation analysis, which affects manageability of risk both for the oil complex and for individual enterprise in the industry. The results of the research may be used for development of software tools, in particular expert and predictive systems. Situational models are needed when oil companies are solving multi-criteria and multifactor problems.
About the authors
Svetlana Evgenievna Germanova
Рeoples’ Friendship University of Russia
Author for correspondence.
Email: germanova-se@rudn.ru
ORCID iD: 0000-0003-2601-6740
Senior Lecturer, Department of Technospheric Security, Agrarian and Technological Institute
6, Miklukho-Maklaya st., Moscow, 117198, Russian FederationTatiana Valeryevna Magdeeva
Рeoples’ Friendship University of Russia
Email: dremova-tv@rudn.ru
ORCID iD: 0000-0002-5584-5321
Senior Lecturer, Department of Technospheric Security, Agrarian and Technological Institute
6, Miklukho-Maklaya st., Moscow, 117198, Russian FederationVadim Gennadievich Pliushchikov
Рeoples’ Friendship University of Russia
Email: pliushchikov-vg@rudn.ru
ORCID iD: 0000-0003-2057-4602
Doctor of Agricultural sciences, Professor, Director of Department of Technospheric Security, Agrarian and Technological Institute
6, Miklukho-Maklaya st., Moscow, 117198, Russian FederationReferences
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