Ranked classification of environmental conditions

A.V. Skatkov, A.A. Bryukhovetskiy, D.V. Moiseev

 Sevastopol State University, RF, Sevastopol, Universitetskaya St., 33

Email: dmitriymoiseev@mail.ru

DOI: 10.33075/2220-5861-2021-1-129-136

UDC 681.3


   This paper discusses the main features associated with the development and research of the device based on the methods of intelligent technology for assessing the state of the natural environment. It should be noted that natural and technical objects, as well as the processes occurring in them, are characterized by high complexity and dynamism, and a significant part of these processes has not yet been fully studied and formalized. Therefore, one of the most important areas of data analysis in this area is the use of artificial neural networks in information and measurement systems. In the works of scientists from various countries, the high efficiency of the use of artificial neural networks in solving individual data processing problems in the classification of environmental conditions is shown.

   The proposed approach is based on methods of nonparametric statistics using rank criteria and will allow for intelligent analysis of data on key environmental indicators, such as hydrometeorological data on the level of pollution and composition of air, soil, maximum permissible emissions of harmful substances, environmental monitoring of anomalies, and others. Static, dynamic, integral, and generalized models of classification of environmental conditions are presented. Further research plans suggest evaluating the impact of sample size on statistical sensitivity, statistical stability, and areas of confident/uncertain recognition, as well as building a decision support system for detecting the G-effect, and considering an adaptive approach to constructing an evaluation matrix.

Keywords: monitoring systems, static model, dynamic model, integral model, generalized model, rank criteria, intelligent technology.

To quote: Skatkov, A.V., A.A. Bryukhovetskiy, and D.V. Moiseev. “Ranked Classification of Environmental Conditions.” Monitoring Systems of Environment no. 1 (March 25, 2021): 129–136. doi:10.33075/2220-5861-2021-1-129-136.

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  1. Gajskij V.A., Gajskij P.V. Mnogomernyj garmonicheskij analiz Fur’e pri izmerenijah polej morskoj sredy. Sistemy kontrolja okruzhajushhej sredy. 2019. Vol. 4 (38). pp. 33–42.
  2. Skatkov A.V., Brjuhoveckij A.A., Moiseev D.V. Intellektual’naja sistema monitoringa dlja reshenija krupnomasshtabnyh nauchnyh zadach v oblachnyh vychislitel’nyh sredah. Informacionno-upravljajushhie sistemy. SPb.: Izd-vo «GUAP», 2017. No 2 (87). pp. 19–25.
  3. Selivanova Z.M. Intellektualizacija informacionno-izmeritel’nyh sistem nerazrushajushhego kontrolja teplofizicheskih svojstv tverdyh materialov. M.: Mashinostroenie-1, 2006. 184 p.
  4. Harisova Z.I. Informacionno-izmeritel’naja sistema dlja granulometricheskogo analiza zhidkih dispersnyh sred na osnove videotehnicheskih sredstv i nejrosetevyh tehnologij: avtoref. dis. … kand. tehn. nauk. Ufa, 2018. 21 p.
  5. Rassell S., Norvig P. Artificial Intelligence: A Modern Approach, 3rd Edition. Pearson Education, 2010. 1095 p.
  6. Orlov S.P., Vasilchenko A.N. Intelligent measuring system for testing and failure analysis of electronic devices. 2016 XIX IEEE International Conference on Soft Computing and Measurements (SCM). IEEE Conference Publications. 2016. Vol. 1. pp. 401–403.
  7. Pososhkov V.L. Ispol’zovanie iskusstvennyh nejronnyh setej dlja statisticheskogo ocenivanija rjada srednesutochnyh osadkov. Sistemy kontrolja okruzhajushhej sredy. 2002. Vol. 4. pp. 146–153.
  8. Werner H., Obach M. New neural network types estimating the accuracy of response for ecological modelling. Ecological Modelling. 2001. Vol. 146. No 1-3. pp. 289–298.
  9. Gevrey M., Dimopoulos I., Lek S. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling. 2003. Vol. 160. No 3. pp. 249–264.
  10. Ciavatta S., Pastres R., Badetti C., Ferrari G. Estimation of phytoplanktonic production and system respiration from data collected by a real-time monitoring network in the lagoon of venice. Ecological Modelling. 2008. Vol. 212. No 1-2. pp. 28–36.
  11. Ajvazjan S.A., Mhitarjan V.S. Prikladnaja statistika i osnovy jekonometriki. M.: Vysshaja shkola, izd-vo «Juniti», 1998. 1000 p.
  12. Kremer N.Sh. Jekonometrika. M.: JuNITI-DANA, 2006. 311 p.
  13. Linnik Ju.V. Metod naimen’shih kvadratov i osnovy matematiko-statisticheskoj teorii obrabotki nabljudenij. Izd. 2-e, dop. i ispr. M.: Gos. izd-vo fiz.-mat. lit., 1962. 349 p.

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