Statistical methods for river runoff prediction |
| |
Authors: | V F Pisarenko A A Lyubushin M V Bolgov T A Rukavishnikova S Kanyu M F Kanevskii E A Savel’eva V V Dem’yanov I V Zalyapin |
| |
Institution: | (1) International Institute of the Theory of Earthquake Prediction and Mathematical Geophysics, Russian Academy of Sciences, Varshavskoe sh. 79, korp. 2, 111355 Moscow, Russia;(2) Institute of Earth Physics, Russian Academy of Sciences, ul. Bol shaya Gruzinskaya 10, 112399 Moscow, Russia;(3) Water Problems Institute, Russian Academy of Sciences, ul. Gubkina 3, GSP-1, 119991 Moscow, Russia |
| |
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.Translated from Vodnye Resursy, Vol. 32, No. 2, 2005, pp. 133–145.Original Russian Text Copyright © 2005 by Pisarenko, Lyubushin, Bolgov, Rukavishnikova, Kanyu, Kanevskii, Savel eva, Dem yanov, Zalyapin. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|