Precipitation concentration index management by adaptive neuro-fuzzy methodology

Dalibor Petković (), Milan Gocic, Slavisa Trajkovic, Miloš Milovančević and Dragoljub Šević
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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

Climatic Change, 2017, vol. 141, issue 4, No 5, 655-669

Abstract: 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.

Keywords: Extreme Learning Machine; Precipitation Amount; Fuzzy Inference System; Model Predictive Control; ANFIS Model (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10584-017-1907-2

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