Application of Neural Networks to Runoff Prediction

Zhu, Mu-Lan; Fujita, Mutsuhiro; Hashimoto, N.

doi:10.1007/978-94-017-3083-9_16

Cited by 50 publications

(25 citation statements)

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“…Previous work has demonstrated that ANNs are adequate to model the rainfall-runoff process [Zhu et al, 1994; Minns and Hall, !996; Shamseldin, 1997]. A comparison between ANN models and traditional models has been made by Hsu et al [1995], who concluded that the ANN approach is more effective and more efficient whenever explicit knowledge of the hydrological subprocess is not required and when the object is to predict streamflow behavior from customary monitored time series of rainfall and flow rate.…”

Section: Paper Number 1998wr900086mentioning

confidence: 99%

River flood forecasting with a neural network model

Campolo¹,

Andreussi²,

Soldati³

1999

Water Resources Research

404

214

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Abstract. A neural network model was developed to analyze and forecast the behavior of the river Tagliamento, in Italy, during heavy rain periods. The model makes use of distributed rainfall information coming from several rain gauges in the mountain district and predicts the water level of the river at the section closing the mountain district. The water level at the closing section in the hours preceding the event was used to characterize the behavior of the river system subject to the rainfall perturbation. Model predictions are very accurate (i.e., mean square error is less than 4%) when the model is used with a 1-hour time horizon. Increasing the time horizon, thus making the model suitable for flood forecasting, decreases the accuracy of the model. A limiting time horizon is found corresponding to the minimum time lag between the water level at the closing section and the rainfall, which is characteristic of each flooding event and depends on the rainfall and on the state of saturation of the basin. Performance of the model remains satisfactory up to 5 hours. A model of this type using just rainfall and water level information does not appear to be capable of predicting beyond this time limit.

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Section: Paper Number 1998wr900086mentioning

confidence: 99%

River flood forecasting with a neural network model

Campolo¹,

Andreussi²,

Soldati³

1999

Water Resources Research

404

214

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“…The structure for all simulation models are three layer BPANN which utilizes a non-linear sigmoid activation function uniformly between the layers. Nodes in the input layer are equal to number of input variables, nodes in hidden layer are varied from 18 (default value by the NP package for 26 input nodes) to approximately double of input nodes [8] and the nodes in the output layer is one as the models provide single output. It was found that 18 hidden nodes give the best results.…”

Section: Ann Model Developmentmentioning

confidence: 99%

“…Hjelmfelt and Wang [7] developed a neural network based on the unit hydrograph theory for the Goodwater Creek watershed in central Missouri. In an application using two neural networks, Zhu et al [8] predicted upper and lower bounds on the flood hydrograph in Butter Creek, New York. Smith [9] used a back-propagation ANN model to predict the peak discharge and the time to peak resulting from a single rainfall pattern.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Event Based Rainfall-Runoff Modeling

Sarkar¹,

Kumar²

2012

JWARP

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The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input variables.

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“…Such applications can be found in many papers published recently (French et al, 1992;Zhu et al, 1994;Hsu et al,1995;Smith and Eli, 1995;Minns and Hall, 1996;Shamseldin, 1997;Campolo et al, 1999;Gautam et al, 2000;Imrie et al, 2000;Chang and Chen, 2001;Shamseldin and OConnor, 2001;Xiong and OConnor, 2002;Campolo et al, 2003;Wilby et al, 2003;Jain et al, 2004). All these works have demonstrated that ANN models are indeed very flexible and sufficiently efficient to simulate the rainfallrunoff processes.…”

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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Application of Neural Networks to Runoff Prediction

Cited by 50 publications

References 1 publication

River flood forecasting with a neural network model

River flood forecasting with a neural network model

Artificial Neural Networks for Event Based Rainfall-Runoff Modeling

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

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