A simple non-linear rainfall-runoff model with a variable gain factor

Ahsan, M.; O’Connor, Kieran M.

doi:10.1016/0022-1694(94)90163-5

Cited by 46 publications

(46 citation statements)

References 7 publications

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“…In each catchment, the models and the combination methods were developed and verified using the same calibration and verification period. Full details of the procedures used in calibrating the four rainfall-runoff models can be found elsewhere (Ahsan and O'Conner 1994, Kachroo 1992, Moore 1985, Senbeta, et al 1999, Tan and O'Connor 1996. LPM, LVGFM and PDISC models were calibrated on minimization of the least squares objective function.…”

Section: Independent Model Data Setsmentioning

confidence: 99%

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Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models

2012

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This paper deals with the application of an innovative method for combining estimated outputs from a number of rainfall-runoff models using Gene Expression Programming (GEP) to perform symbolic regression. The GEP multi-model combination method uses the synchronous simulated river flows from four conventional rainfall-runoff models to produce a set of combined river flow estimates for four different catchments.The four selected models for the multi-model combinations are the Linear Perturbation Model (LPM), the Linearly Varying Gain Factor Model (LVGFM), the Soil Moisture Accounting and Routing (SMAR) Model, and the Probability-Distributed Interacting Storage Capacity (PDISC) model. The first two of these models are 'black-box' models, the LPM exploiting seasonality and the LVGFM employing a storage-based coefficient of runoff. The remaining two are conceptual models. The data of four catchments with different geographical location, hydrological and climatic conditions, are used to test the performance of the GEP combination method.The results of the model using GEP method are compared against original forecasts obtained from the individual models that contributed to the development of the combined model by means of a few global statistics. The findings show that a GEP approach can successfully used as a multi-model combination method. In addition, the GEP combination method also has benefit over other hitherto tested approaches such as an artificial neural network combination method in that its formulation is transparent, can be expressed as a simple mathematical function, and therefore is useable by people who are unfamiliar with such advanced techniques. The GEP combination method is able to combine model outcomes from less accurate individual models and produce a superior river flow forecast.

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Section: Independent Model Data Setsmentioning

confidence: 99%

“…The remaining two are conceptual rainfall-runoff models. Further details on these models and their applications are given by others (Ahsan and O'Connor 1994, Kachroo 1992, Moore 1985, Senbeta, et al 1999, Tan and O'Connor 1996.…”

Section: Introductionmentioning

confidence: 99%

Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models

2012

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“…Descriptions of the models and combination techniques incorporated in the GFMFS are widely available, e.g. in OConnell et al (1970); Nash and Foley (1982); Nash and Barsi (1983); Khan (1986); Kachroo et al (1988); Kachroo (1992, a, b); Liang (1992); Ahsan and OConnor (1994); Liang et al (1994); Zhang et al (1994); Tan and OConnor (1996); Shamseldin (1997); ; Xiong et al (2001). Brief descriptions of these simulation (non-updating) models are given in the Appendix.…”

Section: The Gfmfs Software Package Used In the Studymentioning

confidence: 99%

Assessing the performance of eight real-time updating models and procedures for the Brosna River

Goswami

O’Connor

Bhattarai

et al. 2005

Hydrol. Earth Syst. Sci.

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The flow forecasting performance of eight updating models, incorporated in the Galway River Flow Modelling and Forecasting System (GFMFS), was assessed using daily data (rainfall, evaporation and discharge) of the Irish Brosna catchment (1207 km 2 ), considering their one to six days lead-time discharge forecasts. The Perfect Forecast of Input over the Forecast Lead-time scenario was adopted, where required, in place of actual rainfall forecasts. The eight updating models were: (i) the standard linear Auto-Regressive (AR) model, applied to the forecast errors (residuals) of a simulation (non-updating) rainfall-runoff model; (ii) the Neural Network Updating (NNU) model, also using such residuals as input; (iii) the Linear Transfer Function (LTF) model, applied to the simulated and the recently observed discharges; (iv) the Non-linear Auto-Regressive eXogenous-Input Model (NARXM), also a neural network-type structure, but having wide options of using recently observed values of one or more of the three data series, together with non-updated simulated outflows, as inputs; (v) the Parametric Simple Linear Model (PSLM), of LTF-type, using recent rainfall and observed discharge data; (vi) the Parametric Linear perturbation Model (PLPM), also of LTF-type, using recent rainfall and observed discharge data, (vii) n-AR, an AR model applied to the observed discharge series only, as a naïve updating model; and (viii) n-NARXM, a naïve form of the NARXM, using only the observed discharge data, excluding exogenous inputs. The five GFMFS simulation (non-updating) models used were the non-parametric and parametric forms of the Simple Linear Model and of the Linear Perturbation Model, the Linearly-Varying Gain Factor Model, the Artificial Neural Network Model, and the conceptual Soil Moisture Accounting and Routing (SMAR) model. As the SMAR model performance was found to be the best among these models, in terms of the Nash-Sutcliffe R 2 value, both in calibration and in verification, the simulated outflows of this model only were selected for the subsequent exercise of producing updated discharge forecasts. All the eight forms of updating models for producing leadtime discharge forecasts were found to be capable of producing relatively good lead-1 (1-day ahead) forecasts, with R 2 values almost 90% or above. However, for higher lead time forecasts, only three updating models, viz., NARXM, LTF, and NNU, were found to be suitable, with lead-6 values of R 2 about 90% or higher. Graphical comparisons were made of the lead-time forecasts for the two largest floods, one in the calibration period and the other in the verification period.

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“…(WMO, 1975;WMO, 1992;Singh, 1995;Guo, 2000) and system-theoretic models, such as neural network, SLM, LPM, CLS, LVGFM, MILVGFM, etc. (O'Connor, 1992;Ahsan & O'Connor, 1994;Liang et al, 1994) have been incorporated and stored in the RFFCS forecasting bank as "simulation models". These models are first calibrated and verified based on the historical data of the basin and can then be used for real-time flood forecasting.…”

Section: Real-time Flood Forecasting Componentmentioning

confidence: 99%

A reservoir flood forecasting and control system for China / Un système chinois de prévision et de contrôle de crue en barrage

Guo

Zhang

Chen

et al. 2004

Hydrological Sciences Journal

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Reservoirs play a vital role in flood prevention and disaster relief in China. The objectives of the project described in this study were to establish a reservoir flood forecasting and control system and to design and develop corresponding application software. This paper introduces the current reservoir flood control and operation practice with this system in China. Using modern integration technologies, an application software for this Reservoir Flood Forecasting and Control System (RFFCS) has been developed and updated since 1995. The structure of the system and its main functions, telemetric data acquisition and processing, the hydrological database, flood forecasting, and reservoir operation components are described in detail. The working environment, key technologies and standardization design are emphasized. Having been successfully applied to 212 reservoirs in China, the software has proved to be reliable and user-friendly. In its latest version, the software supports reservoir flood forecasting and flood dispatch decisions. The future research direction and the extension of the software function are also discussed.

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A simple non-linear rainfall-runoff model with a variable gain factor

Cited by 46 publications

References 7 publications

Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models

Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models

Assessing the performance of eight real-time updating models and procedures for the Brosna River

A reservoir flood forecasting and control system for China / Un système chinois de prévision et de contrôle de crue en barrage

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