Using a fuzzy impact assessment for the environment quality evaluation

Kyiv National University of Construction and Architecture
Regional organization of the National Union of Architects of Ukraine

The aim of the research is to develop fuzzy impact models of the natural and anthropogenic influence, which allows to integrate different physical factors, which makes it possible to bring them to a single environmental assessment system and comparison of different assessed areas. Methodology. The basis of the proposed modeling is a traditional approach on the development of such models, which includes conceptual, logical and physical modeling levels. The Unified Modeling Language (UML) is used for conceptual modeling level, which is recommended as the main modeling tool in the set of international standards in geographic information / geomatics and software that supports the interactive mode of UML diagrams creation Visio.  The geospatial database and SQL-functions are implemented and the extension of the standard SQL-99 language with a new data type geometry and built-in functions which provides storage, processing and analysis of geospatial data in database management systems is used. The proposed models are realized in the environment of object-relational DBMS PostgreSQl / Postgis and geographic information system QGIS.  Results. A review of the experience of using fuzzy logic to assess the state of the environment is done. Technological models for computation of indicators of administrative unit provision  by social infrastructure objects, influence of greenery, industrial territories and transport on the environment are offered and realized. An example of approbation of the proposed approach based on OpenStreetMaps open data for the Popasnianskyi distinct of Luhansk region territory is given. Scientific novelty. Theoretical generalizations are made and practical results are received of resolving applied problem of the development of the fuzzy impact assessment model of various factors influence on the environment with use of GIS. Such assessment can be used at the stage of community spatial development strategies preparation to determine the most acceptable development version, as well as to unify the means of strategies implementation monitoring, organically linking local, national and global tasks. Practical significance. The application of the proposed approach of  GRID modeling and fuzzy impact assessment use in assessing the quality of the environment allows to integrate different indicators, compare them, by bringing them into a single evaluation system.

  1. 1. Adriaenssens, V., Goethals, P. L., & De Pauw, N. (2006). Fuzzy knowledge-based models for prediction of Asellus and Gammarus in watercourses in Flanders (Belgium). Ecological Modelling, 195(1-2), 3-10.
    2. Cherepanov, A. S. (2011). Vegetation indexes. Geo¬matics, 2. 98-102.
    3. DBN B.2.2-12: (2019). Planning and development of territories. (in Ukrainian)
    4. DSP-173. (1996). State sanitary rules of planning and development of settlements. (in Ukrainian).
    5. Harrington E. C. (1965). The Desirability Function. Industrial Quality Control, 494-498.
    6. ISO/IEC 13249-3:2011. (2012). Information technology. Database languages - SQL Multimedia and Application Packages. Part 3: Spatial.
    7. Lyashchenko, A. A., Volchko, Є. P., & Kravchenko, Yu.V. (2012). Inconsistent geoinformation models will show ecological factors and pour into a penny assessment of land parcels. Bulletin of Geodesy and Cartography, 1 (76). 37-43. (in Ukrainian). file:///C:/Users/ AB68~1/AppData/Local/Temp/vgtk_2012_1_9.pdf
    8. Lyutik, T. V. (2016). Functions of Harrington's backing as an instrument of the integrated assessment of innovative and scientific and technological warehouse economic potential. History of Science and Biography. (in Ukrainian).
    9. Mario, E., & Giuseppe, S. (2001). Fuzzy approach to the environmental impact evaluation. Ecological Modelling, 136. 131-147.
    10. Olekh, T. M., Gogunsky, V. D., & Rudenko, S. V. (2013). Model of generalized assessment of environmental impact in projects. Management of the development of folding systems, 15. 53-59. (in Ukrainian).
    11. PostgreSQL. URL:
    12. QGIS. URL:
    13. OpenStreetMap. URL:
    14. Petry, F. E., M. A. Cobb (Eds.), & Robinson, V. B. (2005). Fuzzy Modeling with Spatial Information for Geographic Problems. XII, 338 p. 135 illus. 3-540-23713-5. Berlin: Springer,..10.1007/b138243.
    15. Rudner, M., Biedermann, R., Schröder, B., & Kleyer, M. (2007). Integrated grid based ecological and economic (INGRID) landscape model-a tool to support landscape ma-nagement decisions. Environmental Modelling & Soft¬ware, 22(2), 177-187. DOI: 10.1016/j.envsoft.2005.07.016.
    16. Stanovskiy, O., Prokopovitch, I., Olekh, H., Kolesnikova, K., & Sorokina, L. (2018). Procedure for impact assessing on the environment. Праці Одеського політехнічного університету, (1), 99-107. DOI: 10.15276/opu.1.54.2018.14.
    17. Statyukha, G. O, Boyko, T. V, Bendyug, V. I, & Abramov, I. B (2006). Decision-making algorithm for environmental impact assessment. Bulletin of Vinnytsia Polytechnic Institute, 5, 119-123. (in Ukrainian).
    18. The Natural Capital Project (2019). InVEST, user's guide. URL:
    19. Vogiatzakis, N. (2003). GIS-based Modelling and Ecology: A Review of Tools and Methods.
    20. Zaznobina N. (2018). Integral estimates of anthropogenic load on the urban environment as a heterotrophic ecosystem. The author's abstract of the dissertation of a candidate of biological sciences: 03.00.16. N. I. Lobachevsky. (in Russian).