Statistical methods for river runoff prediction

V. F. Pisarenko, A. A. Lyubushin, M. V. Bolgov, T. A. Rukavishnikova, S. Kanyu, M. F. Kanevskii, E. A. Saveleva, V. V. Demyanov, I. V. Zalyapin

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5 Citations (Scopus)

Abstract

Methods used to analyze one type of nonstationary stochastic processes-the periodically correlated process-are considered. Two methods of one-step-forward prediction of periodically correlated time series are examined. One-step-forward predictions made in accordance with an autoregression model and a model of an artificial neural network with one latent neuron layer and with an adaptation mechanism of network parameters in a moving time window were compared in terms of efficiency. The comparison showed that, in the case of prediction for one time step for time series of mean monthly water discharge, the simpler autoregression model is more efficient.

Original languageEnglish
Pages (from-to)115-126
Number of pages12
JournalWater Resources
Volume32
Issue number2
DOIs
Publication statusPublished - Mar 2005

ASJC Scopus subject areas

  • Water Science and Technology

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    Pisarenko, V. F., Lyubushin, A. A., Bolgov, M. V., Rukavishnikova, T. A., Kanyu, S., Kanevskii, M. F., Saveleva, E. A., Demyanov, V. V., & Zalyapin, I. V. (2005). Statistical methods for river runoff prediction. Water Resources, 32(2), 115-126. https://doi.org/10.1007/s11268-005-0016-1