Application of the stochastic self-training procedure for the modelling of extreme floods

Vadim Kuzmin; Pieter Van Gelder; Hafzullah Aksoy; Ismail Kucuk

Application of the stochastic self-training procedure for the modelling of extreme floods

Vadim Kuzmin^*
, Pieter Van Gelder
, Hafzullah Aksoy
, Ismail Kucuk

^*Corresponding author for this work

Russian State Hydrometeorological University

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

2 Citations (Scopus)

Abstract

Modelling techniques valid for ordinary hydrological conditions are often inadequate for catastrophic runoff conditions, as the majority of the parameters included in the modelling schemes are not necessarily applicable to flood conditions. To overcome this problem, using a stochastic self-training procedure (SSTP) is proposed. The procedure is based on the real water balance equation and the statistical model of the water balance. An application of the procedure with data from the Filyos River (Turkey) is presented in this study.

Original language	English
Pages (from-to)	317-322
Number of pages	6
Journal	IAHS-AISH Proceedings and Reports
Issue number	271
Publication status	Published - 2002
Externally published	Yes

Keywords

Extreme floods
Filyos River basin
Kinematic wave forecasting
River flow forecasting
Stochastic models
Stochastic self-training procedure
Turkey

Cite this

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abstract = "Modelling techniques valid for ordinary hydrological conditions are often inadequate for catastrophic runoff conditions, as the majority of the parameters included in the modelling schemes are not necessarily applicable to flood conditions. To overcome this problem, using a stochastic self-training procedure (SSTP) is proposed. The procedure is based on the real water balance equation and the statistical model of the water balance. An application of the procedure with data from the Filyos River (Turkey) is presented in this study.",

keywords = "Extreme floods, Filyos River basin, Kinematic wave forecasting, River flow forecasting, Stochastic models, Stochastic self-training procedure, Turkey",

author = "Vadim Kuzmin and \{Van Gelder\}, Pieter and Hafzullah Aksoy and Ismail Kucuk",

year = "2002",

language = "English",

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journal = "IAHS-AISH Proceedings and Reports",

issn = "0144-7815",

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TY - JOUR

T1 - Application of the stochastic self-training procedure for the modelling of extreme floods

AU - Kuzmin, Vadim

AU - Van Gelder, Pieter

AU - Aksoy, Hafzullah

AU - Kucuk, Ismail

PY - 2002

Y1 - 2002

N2 - Modelling techniques valid for ordinary hydrological conditions are often inadequate for catastrophic runoff conditions, as the majority of the parameters included in the modelling schemes are not necessarily applicable to flood conditions. To overcome this problem, using a stochastic self-training procedure (SSTP) is proposed. The procedure is based on the real water balance equation and the statistical model of the water balance. An application of the procedure with data from the Filyos River (Turkey) is presented in this study.

AB - Modelling techniques valid for ordinary hydrological conditions are often inadequate for catastrophic runoff conditions, as the majority of the parameters included in the modelling schemes are not necessarily applicable to flood conditions. To overcome this problem, using a stochastic self-training procedure (SSTP) is proposed. The procedure is based on the real water balance equation and the statistical model of the water balance. An application of the procedure with data from the Filyos River (Turkey) is presented in this study.

KW - Extreme floods

KW - Filyos River basin

KW - Kinematic wave forecasting

KW - River flow forecasting

KW - Stochastic models

KW - Stochastic self-training procedure

KW - Turkey

UR - https://www.scopus.com/pages/publications/0036128467

M3 - Article

AN - SCOPUS:0036128467

SN - 0144-7815

SP - 317

EP - 322

JO - IAHS-AISH Proceedings and Reports

JF - IAHS-AISH Proceedings and Reports

IS - 271

ER -

Application of the stochastic self-training procedure for the modelling of extreme floods

Abstract

Keywords

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