Statistical methods for river runoff prediction

Autor:	Vasily Demyanov, E. A. Savel’eva, V. F. Pisarenko, Mikhail Bolgov, M. F. Kanevskii, S. Kanyu, I. V. Zalyapin, T. A. Rukavishnikova, Alexey Lyubushin
Rok vydání:	2005
Předmět:	Water discharge Hydrogeology Series (mathematics) Artificial neural network Autoregressive model Time windows River runoff Statistics Applied mathematics Time step Water Science and Technology Mathematics
Zdroj:	Water Resources. 32:115-126
ISSN:	1608-344X 0097-8078
DOI:	10.1007/s11268-005-0016-1
Popis:	Methods used to analyze one type of nonstationary stochastic processes—the periodically corre- lated 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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f3578aa7e892d7828e180209b4968482 https://doi.org/10.1007/s11268-005-0016-1 Zobrazit plný text záznamu Full text from SpringerLink