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

doi:10.1007/s11268-005-0016-1

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

^*Corresponding author for this work

School of Energy, Geoscience, Infrastructure and Society

Research output: Contribution to journal › Article › peer-review

8 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 language	English
Pages (from-to)	115-126
Number of pages	12
Journal	Water Resources
Volume	32
Issue number	2
DOIs	https://doi.org/10.1007/s11268-005-0016-1
Publication status	Published - Mar 2005

ASJC Scopus subject areas

Water Science and Technology

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title = "Statistical methods for river runoff prediction",

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.",

author = "Pisarenko, {V. F.} and Lyubushin, {A. A.} and Bolgov, {M. V.} and Rukavishnikova, {T. A.} and S. Kanyu and Kanevskii, {M. F.} and Saveleva, {E. A.} and Demyanov, {V. V.} and Zalyapin, {I. V.}",

year = "2005",

month = mar,

doi = "10.1007/s11268-005-0016-1",

language = "English",

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pages = "115--126",

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T1 - Statistical methods for river runoff prediction

AU - Pisarenko, V. F.

AU - Lyubushin, A. A.

AU - Bolgov, M. V.

AU - Rukavishnikova, T. A.

AU - Kanyu, S.

AU - Kanevskii, M. F.

AU - Saveleva, E. A.

AU - Demyanov, V. V.

AU - Zalyapin, I. V.

PY - 2005/3

Y1 - 2005/3

N2 - 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.

AB - 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.

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U2 - 10.1007/s11268-005-0016-1

DO - 10.1007/s11268-005-0016-1

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AN - SCOPUS:17844390390

SN - 0097-8078

VL - 32

SP - 115

EP - 126

JO - Water Resources

JF - Water Resources

IS - 2

ER -

Statistical methods for river runoff prediction

Abstract

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