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Type: Journal article
Title: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
Author: Maier, H.
Jain, A.
Dandy, G.
Sudheer, K.
Citation: Environmental Modelling and Software, 2010; 25(8):891-909
Publisher: Elsevier Sci Ltd
Issue Date: 2010
ISSN: 1364-8152
Statement of
Holger R. Maier, Ashu Jain, Graeme C. Dandy, and K.P. Sudheer
Abstract: Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for prediction and forecasting in water resources and environmental engineering. However, despite this high level of research activity, methods for developing ANN models are not yet well established. In this paper, the steps in the development of ANN models are outlined and taxonomies of approaches are introduced for each of these steps. In order to obtain a snapshot of current practice, ANN development methods are assessed based on these taxonomies for 210 journal papers that were published from 1999 to 2007 and focus on the prediction of water resource variables in river systems. The results obtained indicate that the vast majority of studies focus on flow prediction, with very few applications to water quality. Methods used for determining model inputs, appropriate data subsets and the best model structure are generally obtained in an ad-hoc fashion and require further attention. Although multilayer perceptrons are still the most popular model architecture, other model architectures are also used extensively. In relation to model calibration, gradient based methods are used almost exclusively. In conclusion, despite a significant amount of research activity on the use of ANNs for prediction and forecasting of water resources variables in river systems, little of this is focused on methodological issues. Consequently, there is still a need for the development of robust ANN model development approaches. © 2010 Elsevier Ltd. All rights reserved.
Keywords: Artificial neural networks
Water resources
River systems
Modelling process
Model development
Rights: Copyright © 2010 Elsevier Ltd All rights reserved.
DOI: 10.1016/j.envsoft.2010.02.003
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Appears in Collections:Aurora harvest
Civil and Environmental Engineering publications
Environment Institute publications

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