Intelligent technology development of anomalie detection in the ecosystem of Sevastopol water area

A.V. Skatkov1, A.A. Bryukhovetskiy1, Y.E. Shishkin1,2

1 Federal State Educational Institution of Higher Education «Sevastopol State University», Russian Federation, Sevastopol, Universitetskaya St., 33

2 Institute of Natural and Technical Systems, Russian Federation, Sevastopol, Lenin St., 28

Email: iurii.e.shishkin@gmail.com

DOI: 10.33075/2220-5861-2019-1-27-34

UDC 004.9   

Abstract:

   The organization of monitoring systems in modern conditions of increasing scale and complexity of cloud computing environments requires new approaches. Therefore, the problem of developing methods that form the basis for building intelligent technology for detecting anomalies of ecosystems in the water area of the city of Sevastopol in order to ensure continuous monitoring of key environmental indicators presented in the form of non-uniform information flows: hydrometeorological information, data on pollution and composition of air, soil, environmental monitoring, monitoring of maximum permissible emissions of harmful substances to detect changes in the state of the data flow of monitoring considered in this article  is relevant.

   Therefore, to solve these problems, it is proposed to use an integrated approach to the construction of monitoring systems, which has distinctive functional and structural features. The monitoring system being developed has obvious advantages over the known ones. The main differences of the proposed solutions are in using:

  • small sample sizes when measuring the metrics of physical objects of the environment,
  • low complexity and high speed methods,
  • criteria for evaluating information situations of object state changing,
  • adaptive approaches that allow tracking the variability of information states of objects and taking into account the nonstationarity of the environment,
  • method of updating databases containing information on monitored environmental objects.

   The use of the adaptive approach to building intelligent monitoring systems will allow to make the transition to a qualitatively new knowledge, optimize the processes of processing, analysis, and integration of heterogeneous data, increase the reliability and efficiency of the decisions made. The implementation of this approach is based on the application of mathematical models: a non-stationary optimization model of management and a model for ensuring the quality of the monitoring system.

Keywords: monitoring systems, anomalies detection, intelligent technology, big data, modeling of complex system, data mining.

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