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Precipitation concentration index management by adaptive neuro-fuzzy methodology

Author

Listed:
  • Dalibor Petković

    (University of Niš)

  • Milan Gocic

    (University of Nis)

  • Slavisa Trajkovic

    (University of Nis)

  • Miloš Milovančević

    (University of Nis)

  • Dragoljub Šević

    (University of Novi Sad)

Abstract

This paper reconsiders the precipitation concentration index (PCI) in Serbia using precipitation measurements such as the mean winter precipitation amount, annual total precipitation, mean summer precipitation amount, mean spring precipitation amount, mean autumn precipitation amount and the mean of precipitation for the vegetation period (April–September). Potentials for further improvement of PCI prediction lie in the improvement of current prediction strategies. One of the options is the introduction of model predictive control. To manage the PCI, it is good to select factors or parameters that are the most important for PCI estimation and prediction, i.e. to conduct variable selection procedure. In the present study, a regression based on the adaptive neuro-fuzzy inference system (ANFIS) is applied for selection of the most influential PCI inputs based on the precipitation measurements. The effectiveness of the proposed strategy is verified according to the simulation results. The results show that the mean autumn precipitation amount is the most influential for PCI prediction and estimation and could be used for the simplification of predictive methods to avoid multiple input variables.

Suggested Citation

  • Dalibor Petković & Milan Gocic & Slavisa Trajkovic & Miloš Milovančević & Dragoljub Šević, 2017. "Precipitation concentration index management by adaptive neuro-fuzzy methodology," Climatic Change, Springer, vol. 141(4), pages 655-669, April.
    • Handle: RePEc:spr:climat:v:141:y:2017:i:4:d:10.1007_s10584-017-1907-2
    DOI: 10.1007/s10584-017-1907-2
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    References listed on IDEAS

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    1. La Rocca, Michele & Perna, Cira, 2005. "Variable selection in neural network regression models with dependent data: a subsampling approach," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 415-429, February.
    2. Yang, L. & Entchev, E., 2014. "Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique," Applied Energy, Elsevier, vol. 134(C), pages 197-203.
    3. Ahmed El-Shafie & Mahmoud Taha & Aboelmagd Noureldin, 2007. "A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 533-556, March.
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    Cited by:

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    3. Mattar, Mohamed A. & Roy, Dilip Kumar & Al-Ghobari, Hussein M. & Dewidar, Ahmed Z., 2022. "Machine learning and regression-based techniques for predicting sprinkler irrigation's wind drift and evaporation losses," Agricultural Water Management, Elsevier, vol. 265(C).

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