Universal ontological model of the environmental monitoring system

by

A.A. Egorkin1,2,3, S.Yu. Samoylov2, V.P. Evstigneev2

1Institute of Natural and Technical Systems, RF, Sevastopol, Lenin St., 28

2Sevastopol State University, RF, Sevastopol, Universitetskaya St., 33

3FGKVOU VPO «Black Sea Higher Naval Order of the Red Star School named after P.S. Nakhimov»

 RF, Sevastopol, Dybenko St., 1a

DOI: 10.33075/2220-5861-2024-3-33-46

UDC 504.064.36                                              

EDN: https://elibrary.ru/fvazvd

Abstract: 

For the task of digitalizing the area of monitoring and controlling negative environmental impacts, a digital model of an environmental monitoring system was developed based on an ontological approach. The correctness of the model was verified using the example of air pollution monitoring and control. The semantic description of the terms in the modeled domain is based on the W3C standard for any observation system, SOSA (sensor, observation, observation procedure). This standard was extended by introducing new classes that integrate metadata into the digital model for monitoring data quality control. Classes were introduced to enable integration of the developed model with state registries containing data on sanitary protection zones, water protection zones, protected objects, and other zones. Integration with the digital model of the registry of objects with a negative impact on the air environment was also implemented. Such integration allows the model to be adapted to any practical requirements in the field of ecology. The developed digital model can serve as the information core for developing intelligent decision support systems in supervisory activities in the field of environmental protection.

Keywords: ontology model, environmental monitoring, semantic modeling, atmospheric air, digital platform.

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REFERENCES

  1. Order of the Government of the Russian Federation № 3496-r dated 08.12.2021 “On the strategic direction of digital transformation of the ecology and nature management industry by 2030”.
  2. Rasyid M.U.H.A., Sayfudin A., Basofi A., and Sudarsono A. Development of semantic sensor web for monitoring environment conditions. In Proceedings of the 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), Lombok, Indonesia, 28–30 July 2016, pp. 607–612.
  3. Grigor’ev V.I. Avtomatizirovannaya obrabotka gidrometeorologicheskoj informacii (Automated processing of hydrometeorological information). Leningrad: Gidrometeoizdat, 1979, 303 p.
  4. Evstigneev V.P., Evstigneev M.P., Kul’bida N.I., and et al. Sozdanie unificirovannoj bazy meteorologicheskih dannyh Ukrainy (Creation of unified meteorological data base of Ukraine). Trudy FGBU «VNIIGMI-MCD», 2014, Vol. 178, pp.175–184.
  5. Uschold M. Ontology and database schema: What’s the difference? Applied Ontology, 2015, Vol. 10, 243258 p.
  6. Martinez-Cruz C.,·Blanco I.J.,·and Vila M.A. Ontologies versus relational databases: are they so different? A comparison. Artificial Intelligence Review, 2012, Vol. 38, pp. 271–290. DOI 10.1007/s10462-011-9251-9.
  7. Gorshkov S.V. Vvedenie v ontologicheskoe modelirovanie (Introduction to ontological modeling). LLC “TriniData”, 2014–2018, 150 p.
  8. Masmoudi M., Lamine S.B.A., Baazaoui H.Z., and et al. An ontology-based monitoring system for multi-source environmental observations. In: 22nd International conference on knowledge based and intelligent information and engineering systems (KES2018), 3 September 2018 – 5 September 2018 (Belgrade, Serbia), 2018, pp. 1865–1874.
  9. Wang X., Wei H., Chen N., He X., and Tian Z. An Observational Process Ontology-Based Modeling Approach for Water Quality Monitoring. Water, 2020, No. 12 (3), 715 p.
  10. Musackov V.Yu. O postroenii ontologicheskoj bazy znanij promyshlennoj ekologii (On building an ontological knowledge base of industrial ecology). Geoekologiya, 2022, No. 4, pp. 36–39.
  11. RD 52.04.567-2003. Guidance document. Regulations on the State Observation Network. 2003.
  12. Haller A., Janowicz K., Cox S., Danh Le Phuoc, Taylor K., and Lefrançois M. Semantic Sensor Network Ontology. W3C Recommendation 19 October 2017. Available at:
  13. https://www.w3.org/TR/vocab-ssn/#Observations (November 16, 2023).
  14. OWL Working Group. OWL 2 Web Ontology Language Document Overview (Second Edition). World Wide Web Consortium (W3C) Recommendation. 2012. Available at: https://www.w3.org/TR/owl2-overview (November 17, 2023).
  15. Salibekyan S.M. and Petrova S.B. Ob”ektno-atributnaya model’ predstavleniya prostranstvenno-vremennyh otnoshenij mezhdu ob”ektami (Object-attribute model of representation of space-time relations between objects). Prikladnaya informatika, 2016, Vol. 11, No. 3 (63), pp. 103–115.
  16. The OsaWL ontological platform URL: https://t-systema.com/osawl-framework-functionality-2/ (November 16, 2023).
  17. Bonacin R., Nabuco O.F., and Pierozzi I. Ontology models of the impacts of agriculture and climate changes on water resources: Scenarios on interoperability and information recovery. Future Generation Computer Systems, 2016, Vol. 54, pp. 423–434.
  18. Fazliev A.Z., Privezentsev A.I., Gordov E.P., Okladnikov I.G., and Titov A.G. Ontological Description of Meteorological and Climate Data Collections. International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL’2017). Environmental Science, 2017, pp. 266–272.
  19. Yun H., Xu J., Wei M., and Guo Z. Marine Ecological Knowledge Management System Based on Ontology Repository. International Journal of u- and e- Service, Science and Technology, 2015, Vol. 8, No. 2, pp. 153–164.
  20. Horrocks I., Peter F. Patel-Schneider, Boley H., Tabet S., Grosof B., and Dean M. SWRL: A Semantic Web Rule Language Combining OWL and RuleML. 2004. URL:
  21. http://www.w3.org/submissions/2004/SUBM-SWRL-20040521
  22. Ferrer B.R.,·Mohammed W.M., Ahmad M., and et al. Comparing ontologies and databases: a critical review of lifecycle engineering models in manufacturing. Knowledge and Information Systems, 2021, Vol. 63, pp.1271–1304.
  23. Türkmen A. and Can O. Integrating Ontologies and Relational Databases for An Improved Data Access. International Journal of Multidisciplinary Studies and Innovative Technologies, 2022, Vol. 6 (2), pp. 202–207.

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