Review: Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions

  • Authors:

  • Holger R. Maier;Ashu Jain;Graeme C. Dandy;K. P. Sudheer

  • Affiliations:

  • School of Civil, Environmental, and Mining Engineering, The University of Adelaide, Adelaide, SA 5005, Australia;Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208 016, India;School of Civil, Environmental, and Mining Engineering, The University of Adelaide, Adelaide, SA 5005, Australia;Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600 036, India

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2010

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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.