Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction

2005 ◽  
Vol 19 (19) ◽  
pp. 3819-3835 ◽  
Author(s):  
Dae-Il Jeong ◽  
Young-Oh Kim
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rainfall Runoff ◽  
Streamflow Prediction ◽  
Ensemble Streamflow Prediction ◽  
Artificial Neural

scholarly journals Over-parameterisation, a major obstacle to the use of artificial neural networks in hydrology?

2003 ◽  
Vol 7 (5) ◽  
pp. 693-706 ◽  
Author(s):  
E. Gaume ◽  
R. Gosset
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Conceptual Model ◽  
Linear Model ◽  
Case Studies ◽  
Stream Flow ◽  
River Flow ◽  
Prediction Models ◽  
Rainfall Runoff ◽  
Artificial Neural

Abstract. Recently Feed-Forward Artificial Neural Networks (FNN) have been gaining popularity for stream flow forecasting. However, despite the promising results presented in recent papers, their use is questionable. In theory, their “universal approximator‿ property guarantees that, if a sufficient number of neurons is selected, good performance of the models for interpolation purposes can be achieved. But the choice of a more complex model does not ensure a better prediction. Models with many parameters have a high capacity to fit the noise and the particularities of the calibration dataset, at the cost of diminishing their generalisation capacity. In support of the principle of model parsimony, a model selection method based on the validation performance of the models, "traditionally" used in the context of conceptual rainfall-runoff modelling, was adapted to the choice of a FFN structure. This method was applied to two different case studies: river flow prediction based on knowledge of upstream flows, and rainfall-runoff modelling. The predictive powers of the neural networks selected are compared to the results obtained with a linear model and a conceptual model (GR4j). In both case studies, the method leads to the selection of neural network structures with a limited number of neurons in the hidden layer (two or three). Moreover, the validation results of the selected FNN and of the linear model are very close. The conceptual model, specifically dedicated to rainfall-runoff modelling, appears to outperform the other two approaches. These conclusions, drawn on specific case studies using a particular evaluation method, add to the debate on the usefulness of Artificial Neural Networks in hydrology. Keywords: forecasting; stream-flow; rainfall-runoff; Artificial Neural Networks

scholarly journals Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling

2013 ◽  
Vol 17 (1) ◽  
pp. 253-267 ◽  
Author(s):  
N. J. de Vos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Internal State ◽  
Relevant Information ◽  
Training Procedure ◽  
Recurrent Networks ◽  
Eastern United States ◽  
Rainfall Runoff ◽  
Echo State Networks ◽  
Artificial Neural

Abstract. Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall–runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses the recently introduced and conceptually simple echo state networks for streamflow forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent approaches. Two modifications on the echo state network models are made that increase the hydrologically relevant information content of their internal state. The results show that the echo state networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that they can be considered promising alternatives to traditional artificial neural networks in rainfall–runoff modelling.

A new hybrid artificial neural networks for rainfall–runoff process modeling

2013 ◽  
Vol 121 ◽  
pp. 470-480 ◽  
Author(s):  
Shahrokh Asadi ◽  
Jamal Shahrabi ◽  
Peyman Abbaszadeh ◽  
Shabnam Tabanmehr
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Modeling ◽  
Rainfall Runoff ◽  
Artificial Neural
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