Modeling of the daily rainfall-runoff relationship with artificial neural network

Rajurkar, M. P.; Kothyari, Umesh C.; Chaube, U. C.

doi:10.1016/j.jhydrol.2003.08.011

Cited by 248 publications

(121 citation statements)

References 29 publications

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“…It records as a ratio the level of overall agreement between the observed and modelled datasets and is a popular metric that is often expressed in percentage terms using different phrasing e.g. "Error in Volume" (Rajurkar et al, 2004); "Error of Total Runoff Volume" (EV; Lin and Chen, 2004); "Percent Bias" (PBIAS; Yapo et al, 1996;Yu and Yang, 2000); "Deviation of Runoff…”

Section: Equation 18mentioning

confidence: 99%

HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts

Dawson

Abrahart

See

2007

Environmental Modelling & Software

454

244

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Section: Equation 18mentioning

confidence: 99%

HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts

Dawson

Abrahart

See

2007

Environmental Modelling & Software

454

244

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“…The number of neurons between 2 to 6 was chosen by trial and error. All input and output variables were standardized to [0.1, 0.9] scale as follows (Rajurkar et al, 2004):…”

Section: Methodsology Artificial Neural Networkmentioning

confidence: 99%

Uncertainty Analysis of Monthly Streamflow Forecasting

Dehghan¹,

Saghafian²,

Rivaz³

et al. 2014

Curr World Environ

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Streamflow forecasting is an important factor in water resources planning and management. In this study Feed Forward Artificial Neural Network (FFANN) was used for monthly streamflow forecasting. Three scenarios were considered for modeling. Principal Component Analysis (PCA) is used for reducing the model architecture complexity and input data reduction. Twelve statistical criteria were used to evaluate the model performance. Also for quantifying the accuracy of forecast, uncertainty analysis was conducted using Monte Carlo simulation. Results indicated that the model in general is capable to forecast monthly streamflow time series satisfactorily. However the model is underestimated in extreme values. Also, uncertainty analysis shows that the model forecasted monthly streamflow time series properly in the first two scenarios while in the third scenario most of the forecasted values lie out of the upper confidence interval.

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“…In this context, the multilayer feedforward neural network is the most widely used network structure (e.g. Halff et al 1993;Hsu et al 1995;Shamseldin 1997;Rajurkar et al 2004;Vos & Rientjes 2005). Although the possibility of developing models that have a flexible formal structure is certainly attractive, these models still have the enormous drawback of not being transparent, in the sense that their functioning is rather obscure and not easily interpretable.…”

Section: Introductionmentioning

confidence: 99%

Review of the application of fuzzy inference systems in river flow forecasting

Jacquin

Shamseldin

2009

Journal of Hydroinformatics

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This paper provides a general overview about the use of fuzzy inference systems in the important field of river flow forecasting. It discusses the overall operation of the main two types of fuzzy inference systems, namely Mamdani and Takagi-Sugeno-Kang fuzzy inference systems, and the critical issues related to their application. A literature review of existing studies dealing with the use of fuzzy inference systems in river flow forecasting models is presented, followed by some recommendations for future research areas. This review shows that fuzzy inference systems can be used as effective tools for river flow forecasting, even though their application is rather limited in comparison to the popularity of neural networks models. In addition to this, it was found that there are several unresolved issues requiring further attention before more clear guidelines for the application of fuzzy inference systems can be given.

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Modeling of the daily rainfall-runoff relationship with artificial neural network

Cited by 248 publications

References 29 publications

HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts

HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts

Uncertainty Analysis of Monthly Streamflow Forecasting

Review of the application of fuzzy inference systems in river flow forecasting

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