Information Technology of the System Control of Water Use Within River Basins

  • Pavlo Kovalchuk
  • Hanna Balykhina
  • Roman Kovalenko
  • Olena Demchuk
  • Viacheslav Rozhon
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)


One of the key problems of system control of water use is the flushing of river beds with the water from reservoirs. To control the process of flushing, an information technology has been developed. Under the conditions of sustainable development, it provides the optimization of water use and contributes to the ecological rehabilitation within the river basin. The combined operational control of the distribution of water masses and pollution transformation along the river bed is implemented by the balance method. The balance difference equations describe the dynamics of water in the upper and lower layers, its movement and enable to visualize the pollution process. Optimization of the options for operational control is based on a scenario analysis. Multicriteria optimization methods are used based on economic and environmental criteria. Water quality assessment was improved by using the neural network in the monitoring system. The neural network provides a feedback coupling within the control system.

The information technology is adapted to the conditions of water supply for irrigation providing the ecological rehabilitation of the Ingulets River. A scenario analysis of some options for flushing operational control was made. The scenarios are estimated by the economic criterion for saving water resources and the ecological criterion for river rehabilitation. The decision making is based on the Pareto principle. The recommended optimal scenario provides a water supply for the period of 7 days applying small portions of water to prevent pollution sedimentation within the flood plain. The displacement of the mineralized water lens is carried out with less water consumption. It enables to provide a regulatory water supply for irrigation.


Information technology of system control Method of combined control Balance method of pollution distribution Neural networks Structure optimization Scenario analysis Pareto optimal solutions 


  1. 1.
    Kovalchuk, P., Balykhina, H., Kovalchuk, V., Matiash, T.: Water management system in the Ukrainian Danube river area for food and environmental safety. In: International Commission on Irrigation and Drainage, 2nd World Irrigation Forum (WIF2), 6–8 November 2016, Chiang Mai, Thailand, pp. 1602–1612 (2016)Google Scholar
  2. 2.
    Kovalchuk, V.: Special aspects of system management methodology of the territories water regime for protection against flooding. Inductive Model. Complex Syst. 6, 97–106 (2014). (in Ukrainian)Google Scholar
  3. 3.
    Arezki, S.A., Djamila, H.B., Bouziane, B.C.: AQUAZONE: a spatial decision support system for aquatic zone management. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 7(4), 1–13 (2015). Scholar
  4. 4.
    Dukhovny, V., Sokolov, V., Manthrithilake, H.: Integrated Water Resources Management: Putting Good Theory into Real Practice. Central Asian Experience. SIC ICWC, Tashkent (2009)Google Scholar
  5. 5.
    Zgurovsky, M., Pankratova, N.: Basics of System Analysis. BHV, Kyiv (2002). (in Ukrainian)Google Scholar
  6. 6.
    Kovacs, A.: Quo vadis, Danubius? Progress and challenges of nutrient pollution control in the Danube River Basin. In: Fuchs, S., Eyckmanns-Wolter, R. (eds.) International Conference RIVER BASINS 2015, Monitoring, Modelling and Management of Pollutants, Germany, pp. 29–39. Springer, Karlsruhe (2015)Google Scholar
  7. 7.
    The Danube River Basin District Management plan. International Commission for the Protection of the Danube River ICPDR (2009)Google Scholar
  8. 8.
    UE Water Framework Directive 2000/60/EC. Definition of Main TermsGoogle Scholar
  9. 9.
    National paradigm of sustainable development for Ukraine. Public Institution «Institute of Environmental Economics and Sustainable Development of the National Academy of Sciences of Ukraine», Kyiv, 72p. (2016). (in Ukrainian)Google Scholar
  10. 10.
    Zhuchenko, A.I., Osipa, L.V., Cheropkin, E.S.: Design database for an automated control system of typical wastewater treatment processes. Int. J. Eng. Manuf. (IJEM) 7(4), 36–50 (2017). Scholar
  11. 11.
    Zhang, H. Liu, M.: GIS-based emergency management system on abrupt environmental pollution accidents in counties of China. Int. J. Educ. Manag. Eng. (IJEME) 2(8), 31–38 (2012). Scholar
  12. 12.
    Kalburgi, P.B., Shareefa, R.N., Deshannavar, U.B.: Development and evaluation of BOD–DO model for River Ghataprabha near Mudhol (India), using QUAL2K. Int. J. Eng. Manuf. (IJEM) 5(1), 15–25 (2015). Scholar
  13. 13.
    Podinovsky, V., Noghin, V.: Pareto Optimal Solution for Multicriterion Problems. Nauka, Moscow (1982). (in Russian)Google Scholar
  14. 14.
    Bledsoe, B., Beeby, J., Hardie, K.: Evaluation of flushing flows in the fraser river and its tributaries. Technical report, 182p. (2013)Google Scholar
  15. 15.
    Burlaka, B.: The flushing Inhulets river in 2011. Water Manag. Ukraine 5, 17–18 (2011). (in Ukrainian)Google Scholar
  16. 16.
    Olsen, N.R.B.: Two-dimensional numerical modelling of flushing processes in water reservoirs. J. Hydraul. Res. 37(1), 3–16 (1999)CrossRefGoogle Scholar
  17. 17.
    Schaffranek, R.: A flow-simulation model of the Tidal Potomac River. U.S. Geological Survey Water-Supply Paper 2234: A water-quality study of the tidal Potomac river and estuary, 41p. (1987)Google Scholar
  18. 18.
    Seng Mah, D., Putuhena, F., bt Rosli, N.A.: Modelling of river flushing and water quality in a tributary constrained by barrages. Irrig. Drainage Syst. 25(4), 427–434 (2011)CrossRefGoogle Scholar
  19. 19.
    Varga, M., Balogh, S., Csukas, B.: GIS based generation of dynamic hydrological and land patch simulation models for rural watershed areas. Inf. Process. Agric. 3, 1–16 (2016)Google Scholar
  20. 20.
    Ivakhnenko, A., Peka, Y., Vostrov, N.: The Combined Method of Modeling Water and Oil Fields. Naukova Dumka, Kyiv (1984). (in Russian)Google Scholar
  21. 21.
    Kovalchuk, P., Gerus, A.: Identification of the model of pollutants distribution in the surface waters based on field studies. In: Fuchs, S., Eyckmanns-Wolter, R. (eds.) International Conference RIVER BASINS 2015, Monitoring, Modelling & Management of Pollutants, Germany, pp. 101–105. Springer, Karlsruhe (2015)Google Scholar
  22. 22.
    Kovalchuk, P., Balykhina, H., Demchuk, O., Kovalchuk, V.: Modeling of water use and river basin environmental rehabilitation. In: IEEE XIIth International Scientific and Technical Conference Computer Science and Information Technologies (CSIT-2017), 5–8 September 2017, Lviv, Ukraine, pp. 468–472 (2017)Google Scholar
  23. 23.
    Kovalchuk, P., Gerus. A., Kovalchuk, V.: Perseptron model of system environmental assessment of water quality in River Basin. In: Proceedings of 4th International Conference of Inductive Modelling ICIM 2013, pp. 279–284. Springer, Kyiv (2013)Google Scholar
  24. 24.
    Saeed, N.H., Abbod, M.F.: Modelling oil pipelines grid: neuro-fuzzy supervision system. Int. J. Intell. Syst. Appl. (IJISA) 9(10), 1–11 (2017). Scholar
  25. 25.
    Romanenko, V., Zhukynsky, V., Oksiyuk, O.: Methods of Environmental Assessment of Surface Water Quality According to the Appropriate Categories. Symvol-T, Kyiv (1988). (in Ukrainian)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Pavlo Kovalchuk
    • 1
  • Hanna Balykhina
    • 1
  • Roman Kovalenko
    • 1
  • Olena Demchuk
    • 2
  • Viacheslav Rozhon
    • 3
  1. 1.Institute of Water Problems and Land ReclamationNational Academy of Agrarian Sciences of UkraineKyivUkraine
  2. 2.National University of Water and Environmental EngineeringRivneUkraine
  3. 3.State Agency of Water Resources of UkraineKyivUkraine

Personalised recommendations