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20 Artificial Neural Network Concepts in Hydrology

Part 2. Hydroinformatics

  1. Anthony W Minns1,
  2. Michael J Hall2

Published Online: 15 APR 2006

DOI: 10.1002/0470848944.hsa018

Encyclopedia of Hydrological Sciences

Encyclopedia of Hydrological Sciences

How to Cite

Minns, A. W. and Hall, M. J. 2006. Artificial Neural Network Concepts in Hydrology. Encyclopedia of Hydrological Sciences. 2:20.

Author Information

  1. 1

    Marine & Coastal Management, WL Delft Hydraulics, Delft, The Netherlands

  2. 2

    UNESCO-IHE, Department of Water Engineering, Delft, The Netherlands

Publication History

  1. Published Online: 15 APR 2006


The solution of many applied hydrological problems, such as the forecasting of floods, has for several decades been based upon the concepts of linear systems analysis. However, the introduction of informatics tools, such as Artificial Neural Networks (ANNs), with their origins in cognitive sciences and pattern recognition, has made available new lines of investigation. Nevertheless, despite their apparent structural simplicity, the use of ANNs to encapsulate the transformation of rainfall over a catchment into streamflow at its outlet requires at least as much hydrological insight as a conventional physical/conceptual hydrological model. Of particular importance are: the choice of the input data streams; and the maintenance of the ability of the ANN to generalize and extrapolate beyond its training data set. Attention to these aspects invariably provides a model whose goodness-of-fit to an independent testing data set is superior to that of parameter-based hydrological modeling systems.


  • artificial neural networks;
  • rainfall-runoff modelling;
  • training;
  • cross-validation;
  • extrapolation