A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction

Chang, Fi‐John; Chen, Yen‐Chang

doi:10.1016/s0022-1694(01)00350-x

Cited by 226 publications

(99 citation statements)

References 26 publications

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“…FES has been applied with satisfactory results to flood frequency analysis (Shu and Burn, 2004), optimal reservoir operation and flow forecasting (Russell and Campbell, 1996), seasonal water supply forecasting (Mahabir et al, 2003), and modelling of individual components of the hydrological cycle, such as infiltration and surface runoff (Bárdossy, 1996). Promising results have also been obtained through the integration of fuzzy logic with ANNs for short-term rainfallrunoff modelling (Chang and Chen, 2001).…”

Section: Approaches To Hydrological Modellingmentioning

confidence: 99%

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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This article examines the current practice of streamflow modelling, a field under development for over a century. A sample of the wide range of assessment and planning applications of streamflow models is presented. The diversity in the use of these models is mirrored in the diversity of model complexity, and modelling approaches ranging from empirical to physically based and from lumped to fully distributed are described with examples. Predictions derived from hydrological models are subject to many sources of error; these are discussed along with methods for error minimization or anticipation. Model error is generally quantified using an ensemble of forecasts meant to sample the range of predictive uncertainty. This ensemble can be used to generate reliable probabilistic forecasts of hydrological quantities if all sources of error are accounted for. To date, applications of ensemble methods in streamflow forecasting have typically focused on only one or two error sources. A challenge will be to develop ensemble streamflow forecasts that sample a wider range of predictive uncertainty.

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Section: Approaches To Hydrological Modellingmentioning

confidence: 99%

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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“…They concluded that time series analysis is far more accurate than simple rainfall predictions of a heuristic nature. Chang and Chen (2001) proposed a counter-propagation fuzzy-neural network capable of automatically generating rules for use in clustering input data to enable streamflow prediction. Nayak et al (2005) employed fuzzy computation in the development of a realtime flood forecasting model.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm

Ouyang

2016

Nat. Hazards Earth Syst. Sci.

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Abstract. The forecasting of inundation levels during typhoons requires that multiple objectives be taken into account, including the forecasting capacity with regard to variations in water level throughout the entire weather event, the accuracy that can be attained in forecasting peak water levels, and the time at which peak water levels are likely to occur. This paper proposed a means of forecasting inundation levels in real time using monitoring data from a water-level gauging network. ARMAX was used to construct water-level forecast models for each gauging station using input variables including cumulative rainfall and water-level data from other gauging stations in the network. Analysis of the correlation between cumulative rainfall and water-level data makes it possible to obtain the appropriate accumulation duration of rainfall and the time lags associated with each gauging station. Analyses on cross-site water levels as well as on cumulative rainfall enable the identification of associate sites pertaining to each gauging station that share high correlations with regard to water level and low mutual information with regard to cumulative rainfall. Water-level data from the identified associate sites are used as a second input variable for the water-level forecast model of the target site. Three indices were considered in the selection of an optimal model: the coefficient of efficiency (CE), error in the stage of peak water level (ESP), and relative time shift (RTS). A multiobjective genetic algorithm was employed to derive an optimal Pareto set of models capable of performing well in the three objectives. A case study was conducted on the Xinnan area of Yilan County, Taiwan, in which optimal water-level forecast models were established for each of the four waterlevel gauging stations in the area. Test results demonstrate that the model best able to satisfy ESP exhibited significant time shift, whereas the models best able to satisfy CE and RTS provide accurate forecasts of inundations when variations in water level are less extreme.

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“…Use of this technique for rainfall-runoff and river flow time series predictions has been reported by many researchers (e.g. Valenca & Ludermir, 2000;Chang & Chen, 2001Nayak et al, 2004Nayak et al, , 2005.…”

Section: Introductionmentioning

confidence: 99%

Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction

Pramanik

Panda

2009

Hydrological Sciences Journal

112

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Appropriate outflow from a barrage should be maintained to avoid flooding on the downstream side during the rainy season. Due to the nonlinear and fuzzy behaviour of hydrological processes, and in cases of scarcity of relevant data, it is difficult to simulate the desired outflow using physically-based models. Artificial intelligence techniques, namely artificial neural networks (ANN) and an adaptive neurofuzzy inference system (ANFIS), were used in the reported study to estimate the flow at the downstream stretch of a river using flow data for upstream locations. Comparison of the performance of ANN and ANFIS was made by estimating daily outflow from a barrage located in the downstream region of Mahanadi River basin, India, using daily release data from the Hirakud Reservoir, located some distance upstream of the barrage. To obtain the best input-output mapping, five different models with various input combinations were evaluated using both techniques. The significance of the contribution of two upstream tributaries to barrage outflow estimation was also evaluated. Three feed-forward back-propagation training algorithms were used to train the models. Standard performance indices, such as correlation coefficient, index of agreement, root mean square error, modelling efficiency and percentage deviation in peak flow, were used to compare the performance of the models, as well as the training techniques. The results revealed that the neural network with conjugate gradient algorithm performs better than Levenberg-Marquardt and gradient descent algorithms. The model which considers as input the reservoir release up to three antecedent time steps produced the best results. It was found that barrage outflow could be better estimated by the ANFIS than by the ANN technique.

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A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction

Cited by 226 publications

References 26 publications

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm

Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction

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