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

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Advances in Computer Science for Engineering and Education (ICCSEEA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 754))

Abstract

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.

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References

  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)

    Article  Google Scholar 

  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. 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. Kamble1, B., Irmak, A., Hubbard, K., Gowda, P.: Irrigation scheduling using remote sensing data assimilation approach. Adv. Remote Sens. 2, 258–268 (2013)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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). http://www.enorasis.eu/. Accessed 15 Nov 2017

  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. 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)

    Article  Google Scholar 

  10. FAO CROPWAT 8.0. http://www.fao.org/land-water/databases-and-software/cropwat/en/, Accessed 18 Dec 2016

  11. Baruth, B., Genovese, G., Leo, O.: CGMS version 9.2 - user manual and technical documentation. OPOCE, Luxembourg (2007)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. 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. 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). https://doi.org/10.5815/ijieeb.2014.04.06

    Article  Google Scholar 

  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). https://doi.org/10.5815/ijmsc.2015.01.02

    Article  Google Scholar 

  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). https://doi.org/10.5815/ijitcs.2013.01.05

    Article  Google Scholar 

  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. 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. 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. 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. OpenWeatherMap. https://openweathermap.org/. Accessed 06 Nov 2017

  26. AccuWeather. https://www.accuweather.com/en/. Accessed 06 Nov 2017

  27. Sinoptik, U.A.: https://ua.sinoptik.ua/. Accessed 06 Nov 2017

  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. FAO CLIMWAT 2.0. http://www.fao.org/land-water/databases-and-software/climwat-for-cropwat/en/. Accessed 11 May 2017

  30. RP5. http://rp5.ua/Weather_archive_in_Kiev_airport. Accessed 01 Mar 2017

  31. WRF (The Weather Research and Forecasting Model). https://www.mmm.uc-ar.edu/weather-research-and-forecasting-model. Accessed 10 Oct 2017

  32. Trucharev, R.I.: Models of Decision Making in Conditions of Uncertainty. Nauka, Moscow (1981). (In Russian)

    Google Scholar 

  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)

    Article  Google Scholar 

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Acknowledgment

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

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Authors and Affiliations

  1. Institute of Water Problems and Land Reclamation, Vasylkivska str., 37, Kyiv, 03022, Ukraine

    Volodymyr Kovalchuk & Oleksandr Voitovich

  2. National University of Water and Environmental Engineering, Soborna str., 11, Rivne, 33028, Ukraine

    Olena Demchuk

  3. National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”, Prospect Peremogy, 37, Kiev, 03056, Ukraine

    Dmytro Demchuk

Authors
  1. Volodymyr Kovalchuk
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  2. Olena Demchuk
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  3. Dmytro Demchuk
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  4. Oleksandr Voitovich
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Corresponding author

Correspondence to Volodymyr Kovalchuk .

Editor information

Editors and Affiliations

  1. School of Educational Information Technology, Central China Normal University, Wuhan, Hubei, China

    Zhengbing Hu

  2. Mechanical Engineering Research Institute, Russian Academy of Sciences, Moscow, Russia

    Sergey Petoukhov

  3. Faculty of Applied Mathematics, National Technical University of Ukraine “Igor Sikorsky Kiev Polytechnic Institute”, Kiev, Ukraine

    Ivan Dychka

  4. Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Ft. Lauderdale, Florida, USA

    Matthew He

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Kovalchuk, V., Demchuk, O., Demchuk, D., Voitovich, O. (2019). Data Mining for a Model of Irrigation Control Using Weather Web-Services. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education. ICCSEEA 2018. Advances in Intelligent Systems and Computing, vol 754. Springer, Cham. https://doi.org/10.1007/978-3-319-91008-6_14

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