New tools for neurohydrologists: using 'network pruning' and 'model breeding' algorithms to discover optimum inputs and architectures
River flow prediction and forecasting are important environmental functions. The successful application of detailed physical-mathematical models offers one possible source for the provision of these estimates. But such models are often too complex, or too demanding in terms of data and computer requirements, for practical implementation purposes. Simpler approaches offered through 'conceptual' and 'black-box' modelling are thus attractive alternatives. Foremost in this re-emergent field is the use of computational intelligence tools such as neural networks and genetic algorithms - which are being investigated as potential mechanisms for the provision of detailed hydrological estimates.
However, irrespective of recent computational and methodological advances, several fundamental problems still need to be addressed - such as the selection of an optimal neural network architecture for each given task. A number of simple and novel solutions to this problem have been put forward in the guise of built-in functions and add-on software tools. These computational resources can be used to diminish the amount of subjective guesswork that is needed to resolve difficult network design issues. It is therefore important that scientists begin to examine the various options that are now available and in particular the extent to which the application of such devices can be used to assist the hydrological modelling effort.
This paper provides some numerical results from an initial investigation into the use of automated neural network design tools for the creation of improved network architectures based on a 'one-step-ahead prediction' of continuous flow records for the Upper River Wye catchment 1984-6. Four alternative neural network modelling strategies were implemented; the first investigation involved using standard procedures to create a set of standard networks; in the next two investigations two simple pruning algorithms were used to create a set of more efficient architectures, and in the last investigation a genetic algorithm package was used to breed a set of optimised neural network modelling solutions based on random mutation and survival of the fittest.