Predicting and forecasting flow discharge at sites receiving significant lateral inflow (Articolo in rivista)

Type

• Prodotto della ricerca (Classe)
• Articolo in rivista (Classe)

Label

• Predicting and forecasting flow discharge at sites receiving significant lateral inflow (Articolo in rivista) (literal)

Anno

• 2007-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1002/hyp.6320 (literal)

Alternative label

• Tayfur G., Moramarco T., Singh V.P. (2007)
  Predicting and forecasting flow discharge at sites receiving significant lateral inflow
  in Hydrological processes (Print)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Tayfur G., Moramarco T., Singh V.P. (literal)

Pagina inizio

• 1848 (literal)

Pagina fine

• 1859 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 21 (literal)

Rivista

• Hydrological processes (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

• Published online in Wiley InterScience (literal)

Note

• ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Department Civil Engineering, Izmir Institute of Technology, Urla, Izmir, 35340, Turkey ; Research Institute for Hydrogeological Protection, National Research Council, Via Madonna Alta, 126, 06128 Perugia, Italy ; Department of Civil and Environmental Engineering,Louisiana State University, Baton Rouge, LA 70803, USA (literal)

Titolo

• Predicting and forecasting flow discharge at sites receiving significant lateral inflow (literal)

Abstract

• Two models, one linear and one non-linear, were employed for the prediction of flow dischargehydrographs at sites receiving significant lateral inflow. The linear model is based on a rating curve and permits a quick estimation of flow at a downstream site. The non-linear model is based on a multilayer feed-forward back propagation (FFBP) artificial neural network (ANN) and uses flow-stage data measured at the upstream and downstream stations. ANN predicted the real-time storm hydrographs satisfactorily and better than did the linear model. The results of sensitivity analysis indicated that when the lateral inflow contribution to the channel reach was insignificant, ANN, usingonly the flow-stage data at the upstream station, satisfactorily predicted the hydrograph at the downstream station. The prediction error of ANN increases exponentially with the difference between the peak discharge used in training and that used intesting. ANN was also employed for flood forecasting and was compared with the modified Muskingum model (MMM). For a 4-hlead time, MMM forecasts the floods reliably but could not be applied to reaches for lead times greater than the wave travel time. Although ANN and MMM had comparable performances for an 8-h lead time, ANN is capable of forecasting floods with lead times longer than the wave travel time. (literal)

Prodotto di

• Stime dell'umidità del suolo (Terminato) (TA.P06.006.002) (Modulo)
• Istituto di ricerca per la protezione idrogeologica (IRPI) (Istituto)
• Previsione eventi idrologici estremi e valutazione rischio idraulico (TA.P05.004.002) (Modulo)

Autore CNR

• TOMMASO MORAMARCO (Unità di personale interno)

Incoming links:

Autore CNR di

• TOMMASO MORAMARCO (Unità di personale interno)

Prodotto

• Istituto di ricerca per la protezione idrogeologica (IRPI) (Istituto)
• Previsione eventi idrologici estremi e valutazione rischio idraulico (TA.P05.004.002) (Modulo)
• Stime dell'umidità del suolo (Terminato) (TA.P06.006.002) (Modulo)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Hydrological processes (Rivista)