River flow forecasting through conceptual models part II - The Brosna catchment at Ferbane

O’Connell, P. E.; Nash, J.E.; Farrell, Jody

doi:10.1016/0022-1694(70)90221-0

Cited by 119 publications

(55 citation statements)

References 2 publications

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“…This model was originally proposed by O'Connell et al (1970) and it has been extensively tested, modified and further developed at the National University of Ireland (NUI), Galway. In the present study, a modified version of the SMAR model is used.…”

Section: The Smar Modelmentioning

confidence: 99%

“…Since the ARXM updating procedure is more general than the AR model updating procedure, the ARXM updating procedure is used in the present study as a convenient benchmark against which the performance of the non-linear neural network updating procedure can be compared. The daily estimated discharge forecasts of the lumped conceptual Soil Moisture Accounting and Routing (SMAR) model (O'Connell et al, 1970;Kachroo, 1992;Khan, 1986;Liang, 1992) for five catchments of different sizes and climatic conditions are used in this comparison of updating procedures. Three of these five catchments are located in China, the remaining two catchments being located in Ireland and Thailand.…”

Section: Introductionmentioning

confidence: 99%

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A non-linear neural network technique for updating of river flow forecasts

Shamseldin

O’Connor

2001

Hydrol. Earth Syst. Sci.

100

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A non-linear Auto-Regressive Exogenous-input model (NARXM) river flow forecasting output-updating procedure is presented. This updating procedure is based on the structure of a multi-layer neural network. The NARXM-neural network updating procedure is tested using the daily discharge forecasts of the soil moisture accounting and routing (SMAR) conceptual model operating on five catchments having different climatic conditions. The performance of the NARXM-neural network updating procedure is compared with that of the linear Auto-Regressive Exogenous-input (ARXM) model updating procedure, the latter being a generalisation of the widely used Auto-Regressive (AR) model forecast error updating procedure. The results of the comparison indicate that the NARXM procedure performs better than the ARXM procedure.

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Section: The Smar Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A non-linear neural network technique for updating of river flow forecasts

Shamseldin

O’Connor

2001

Hydrol. Earth Syst. Sci.

100

View full text Add to dashboard Cite

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“…2). The NSE measures the fraction of the variance of the observed flows explained by the model in terms of the relative magnitude of the residual variance (noise) to the variance of the flows (information); the optimal value is 1.0 and values should be larger than 0.0 to indicate minimally acceptable performance (Nash and Sutcliffe, 1970;O'Connell et al, 1970).…”

Section: Statistical Criteria Used To Assess Models' Performancementioning

confidence: 99%

A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

Saleh

Ramaswamy

Georgas

et al. 2016

Hydrol. Earth Syst. Sci.

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“…SimHyd models daily runoff (surface runoff, interflow and baseflow) using daily precipitation and potential evapotranspiration as input data Wang et al, 2006). SMAR model provides daily estimates of surface runoff (overland flow, saturation excess runoff and saturated throughflow from perched groundwater conditions), groundwater discharge, evapotranspiration and leakage from the soil profile for the catchment as a whole (O'Connell et al, 1970;Kachroo and Liang, 1992;Tan and O'Connor, 1996;Tuteja and Cunnane, 1999). Both SimHyd and SMAR include the infiltration excess and saturation excess mechanisms.…”

Section: Model Comparisonmentioning

confidence: 99%

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

Jiang

Liu

et al. 2015

Environmental Modelling & Software

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a b s t r a c tWe present a study on the Hydro-Informatic Modelling System (HIMS) rainfall-runoff model for a semiarid region. The model includes nine parameters in need of calibration. A master-slave swarms, shuffling evolution algorithm based on self-adaptive dynamic particle swarm optimization (MSSE-SDPSO) is proposed to derive model parameters. In comparison with SCE-UA, PSO, MSSE-PSO and MSSE-SPSO algorithms, MSSE-SDPSO has faster convergence and more stable performance. The model is used to simulate discharge in the Luanhe River basin, a semiarid region. Compared with the SimHyd and SMAR models, HIMS model has the highest Nash-Sutcliffe efficiencies (NSE) and smallest relative errors (RE) of volumetric fitness for the periods of calibration and verification. In addition, the studies indicate that the HIMS model with all-gauge data improves runoff prediction compared with single-gauge data. A distributed HIMS model performs better than a lumped one. Finally, the Morris method is used to analyze model parameters sensitivity for the objective functions NSE and RE.

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River flow forecasting through conceptual models part II - The Brosna catchment at Ferbane

Cited by 119 publications

References 2 publications

A non-linear neural network technique for updating of river flow forecasts

A non-linear neural network technique for updating of river flow forecasts

A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

Rainfall-runoff modeling, parameter estimation and sensitivity analysis in a semiarid catchment

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