Data Mining for a Model of Irrigation Control Using Weather Web-Services

  • Volodymyr Kovalchuk
  • Olena Demchuk
  • Dmytro Demchuk
  • Oleksandr Voitovich
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)


The article deals with obtaining forecast weather data, its processing and use in mathematical models for irrigation management in the application of Decision Support System. The data obtained from the weather service databases on temperature and humidity are summarized on the basis of potential evapotranspiration calculations. Forecast data on precipitation is handled under uncertainty. On the basis of the weather forecast data, moisture transfer is modeled, soil moisture is predicted, that is, new knowledge is obtained about the state of soil moisture, on the basis of which Decision Support System generates a certain management solution. Due to the Internet and the use of the online regime, the decision maker does not directly process large arrays of weather information, but receives Decision Support System solutions as quickly and easily as possible.


Data mining Weather web-services Model of irrigation control Weather data processing under uncertainty Calculation of evapotranspiration DSS 



Publications are based on the research provided by the grant support of the State Fund for Fundamental Research (project F76/95-2017).


  1. 1.
    Shang, S., Li, X., Mao, X., Lei, Z.: Simulation of water dynamics and irrigation scheduling for winter wheat and maize in seasonal frost areas. Agric. Water Manag. 68, 117–133 (2004)CrossRefGoogle Scholar
  2. 2.
    Kovalchuk, P., Balykhina, H., Kovalchuk, V., Matyash, T.: Water management system in the Ukrainian Danube river area for food and environmental safety. In: Proceedings of the 2nd World Irrigation Forum (WIF2), pp. 1–10. ICID, Chiang Mai, Thailand (2016)Google Scholar
  3. 3.
    Steduto, P., Raes, D., Hsiao, T.C., Fereres, E.: AquaCrop: concepts, rationale and operation. In: Steduto, P., Hsiao, T.C., Fereres, E., Raes, D. (Eds.) Crop Yield Response to Water. FAO irrigation and drainage paper no. 66, pp. 17–49. FAO, Rome (2012)Google Scholar
  4. 4.
    Kamble1, B., Irmak, A., Hubbard, K., Gowda, P.: Irrigation scheduling using remote sensing data assimilation approach. Adv. Remote Sens. 2, 258–268 (2013)CrossRefGoogle Scholar
  5. 5.
    Rallo, G., Agnese, C., Blanda, F., Minacapilli, M., Provenzano, G.: Agro-hydrological models to schedule irrigation of mediterranean tree crops. Ital. J. Agrometeorology 1, 11–21 (2010)Google Scholar
  6. 6.
    Pulido-Calvo, I., Roldan, J., Lopez-Luque, R., Gutierrez-Estrada, J.C.: Demand forecasting for irrigation water distribution systems. J. Irrig. Drainage Eng. 129(6), 422–431 (2003)CrossRefGoogle Scholar
  7. 7.
    ENORASIS (Environmental Optimization of irrigation Management with the Combined use and Integration of High precision Satellite Data, Advanced Modeling, Process Control and Business Innovation). Accessed 15 Nov 2017
  8. 8.
    Zhovtonog, O.I., Filipenko, L.A., Demenkova, T.F., Didenko, N.O.: Use of the information system “GIS irrigation” and the IRRIMET module for the internet weather station for operational irrigation planning in sprinkling. Taurian Sci. Bull. 92, 159–165 (2015). (In Ukrainian)Google Scholar
  9. 9.
    Silva, D., Meza, F.J., Varas, E.: Estimating reference evapotranspiration (ETo) using numerical weather forecast data in central Chile. J. Hydrol. 382, 64–71 (2010)CrossRefGoogle Scholar
  10. 10.
  11. 11.
    Baruth, B., Genovese, G., Leo, O.: CGMS version 9.2 - user manual and technical documentation. OPOCE, Luxembourg (2007)Google Scholar
  12. 12.
    Van Diepen, C.A., Rappoldt, C., Wolf, J., van Keulen, H.: WOFOST: a simulation model of crop production. Soil Use Manag. 5(1), 16–24 (1989)CrossRefGoogle Scholar
  13. 13.
    Dhore1, A., Byakude, A., Sonar, B., Waste, M.: Weather prediction using the data mining techniques. Int. Res. J. Eng. Technol. (IRJET) 4(5), 2562–2565 (2017)Google Scholar
  14. 14.
    Liao, S.-H., Chu, P.-H., Hsiao, P.-Y.: Data mining techniques and applications – a decade review from 2000 to 2011. Rev. Expert Syst. Appl. 39, 11303–11311 (2012)CrossRefGoogle Scholar
  15. 15.
    Sara Kutty, T.K., Hanumanthappa, M.: Optimal water allocation using data mining techniques. a survey. Int. J. Emerg. Res. Manag. Technol. 6(8), 226–229 (2017)Google Scholar
  16. 16.
    Khan, M., Islam, M.Z., Hafeez, M.: Irrigation water requirement prediction through various data mining techniques applied on a carefully pre-processed dataset. J. Res. Pract. Inf. Technol. 1, 1–13 (2013)Google Scholar
  17. 17.
    Bhatt, N., Virparia, P.V.: A survey based research for data mining techniques to forecast water demand in irrigation. I.J. Comput. Sci. Mob. Appl. 3(8), 14–18 (2015)Google Scholar
  18. 18.
    Lazri, M., Ameur, S., Brucker, J.M.: Analysis of the time trends of precipitation over mediterranean region. Int. J. Inf. Eng. Electron. Bus. (IJIEEB), 6(4), 38–44 (2014). Scholar
  19. 19.
    Vamsi Krishna, G.: Prediction of rainfall using unsupervised model based approach using k-means algorithm. Int. J. Math. Sci. Comput. (IJMSC) 1(1), 11–20 (2015). Scholar
  20. 20.
    Sultana, S.H., Ali, M.S., Hena, M.A., Rahman, M.M.: A simple model of mapping of land surface temperature from satellite digital images in Bangladesh. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 5(1), 51–57 (2013). Scholar
  21. 21.
    Rinaldi, M., He, Z.: Decision support systems to manage irrigation in agriculture. In: Sparks, D. (ed.) Advances in Agronomy, vol. 123, pp. 229–279. Academic Press, Burlington (2014)Google Scholar
  22. 22.
    Eitzinger, J., Thaler, S., Orlandini, S., Nejedlik, P., Kazandjiev, V., Vucetic, V., Sivertsen, T.H.: Agroclimatic indices and simulation models. In: Nejedlik, P., Orlandini, S. (eds.) Survey of Agrometeorological Practices and Applications in Europe Regarding Climate Change Impacts, pp. 15–115. European Science Foundation, Florence (2008)Google Scholar
  23. 23.
    Allen, R.G., Pereira, L.S., Raes, D., Smith, M.: Crop evapotranspiration–guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, Rome, Italy (1998)Google Scholar
  24. 24.
    Tsivinsky, G.V., Pendak, N.V., Idayatov, V.A.: Instruction on Operative Calculation of Irrigation Regimes and Forecast of Irrigation of Agricultural Crops due to Lack of Moisture Stores, 2nd edn. Ukrainian Ecological League, Kherson (2010). (In Ukrainian)Google Scholar
  25. 25.
    OpenWeatherMap. Accessed 06 Nov 2017
  26. 26.
    AccuWeather. Accessed 06 Nov 2017
  27. 27.
    Sinoptik, U.A.: Accessed 06 Nov 2017
  28. 28.
    Kovalchuk, V.P.: Ecological and economic optimization of irrigation regimes taking into account the quality of groundwater. In: Ways to Improve the Efficiency of Irrigated Agriculture, vol. 50, pp. 81–88 (2013). (In Russian)Google Scholar
  29. 29.
  30. 30.
  31. 31.
    WRF (The Weather Research and Forecasting Model). Accessed 10 Oct 2017
  32. 32.
    Trucharev, R.I.: Models of Decision Making in Conditions of Uncertainty. Nauka, Moscow (1981). (In Russian)Google Scholar
  33. 33.
    Car, N.J., Christen, E.W., Hornbuckle, J.W., Moore, G.A.: Using a mobile phone short messaging service (SMS) for irrigation scheduling in Australia–farmers’ participation and utility evaluation. Comput. Electron. Agri. 84, 132–143 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Volodymyr Kovalchuk
    • 1
  • Olena Demchuk
    • 2
  • Dmytro Demchuk
    • 3
  • Oleksandr Voitovich
    • 1
  1. 1.Institute of Water Problems and Land ReclamationKyivUkraine
  2. 2.National University of Water and Environmental EngineeringRivneUkraine
  3. 3.National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”KievUkraine

Personalised recommendations