River flow forecasting. Part 2. Algebraic development of linear modelling techniques

Kachroo, R.K.; Liang, G.C.

doi:10.1016/0022-1694(92)90147-n

Cited by 58 publications

(38 citation statements)

References 7 publications

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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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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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“…There exist a large number of potentially useful models for this purpose (as summarized by Singh and Woolhiser [5], or see the extensive list in Gouweleeuw et al [6]). They are basically either time series models, where the output is determined from known input data by means of transition probabilities (time series models), sometimes as artificial neural networks (ANN) [7] or conceptual rainfall-runoff models, usually based on applying linear systems theory (one or more Nash Cascades, see [8] for an extensive discussion). A strong case can be made to not rely on a given model but to develop models based on basic hydrological principles, which optimally account for physical and climatic conditions of the region [9][10][11], perhaps starting with an elementary model, which may be upgraded with increased observational evidence (model development along the "axis of complexity" [12]).…”

Section: Model Selectionmentioning

confidence: 99%

Uncertainty Analysis of Multi-Model Flood Forecasts

Plate

Shahzad

2015

Water

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This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf), calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf) by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables.

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“…is the most widely used paradigm in real time flow forecasting (Kachroo and Liang, 1992;WMO, 1992;Xiong and OConnor, 2002).…”

Section: Updating Scheme No1mentioning

confidence: 99%

“…These errors result from inadequacies in the model structure, incorrect estimation of the model parameters, errors in the data or, indeed, the absence of any consistent relationship in the data (Kachroo and Liang, 1992). Observation of the structure of the error persistence can provide the basis for an updating procedure.…”

Section: Introductionmentioning

confidence: 99%

Comparison of three updating schemes using artificial neural network in flow forecasting

Xiong

O’Connor

2004

Hydrol. Earth Syst. Sci.

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Three updating schemes using artificial neural network (ANN) in flow forecasting are compared in terms of model efficiency. The first is the ANN model in the simulation mode plus an autoregressive (AR) model. For the ANN model in the simulation model, the input includes the observed rainfall and the previously estimated discharges, while the AR model is used to forecast the flow simulation errors of the ANN model. The second one is the ANN model in the updating mode, i.e. the ANN model uses the observed discharge directly together with the observed rainfall as the input. In this scheme, the weights of the ANN model are obtained by optimisation and then kept fixed in the procedure of flow forecasting. The third one is also the ANN model in the updating mode; however, the weights of the ANN model are no longer fixed but updated at each time step by the backpropagation method using the latest forecast error of the ANN model. These three updating schemes are tested for flow forecasting on ten catchments and it is found that the third updating scheme is more effective than the other two in terms of their efficiency in flow forecasting. Moreover, compared to the first updating scheme, the third scheme is more parsimonious in terms of the number of parameters, since the latter does not need any additional correction model. In conclusion, this paper recommends the ANN model with the backpropagation method, which updates the weights of ANN at each time step according to the latest forecast error, for use in real-time flow forecasting.

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River flow forecasting. Part 2. Algebraic development of linear modelling techniques

Cited by 58 publications

References 7 publications

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

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

Uncertainty Analysis of Multi-Model Flood Forecasts

Comparison of three updating schemes using artificial neural network in flow forecasting

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