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An application of pruning in the design of neural networks for real time flood forecasting

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Abstract

We propose the application of pruning in the design of neural networks for hydrological prediction. The basic idea of pruning algorithms, which have not been used in water resources problems yet, is to start from a network which is larger than necessary, and then remove the parameters that are less influential one at a time, designing a much more parameter-parsimonious model. We compare pruned and complete predictors on two quite different Italian catchments. Remarkably, pruned models may provide better generalization than fully connected ones, thus improving the quality of the forecast. Besides the performance issues, pruning is useful to provide evidence of inputs relevance, removing measuring stations identified as redundant (30–40% in our case studies) from the input set. This is a desirable property in the system exercise since data may not be available in extreme situations such as floods; the smaller the set of measuring stations the model depends on, the lower the probability of system downtimes due to missing data. Furthermore, the Authority in charge of the forecast system may decide for real-time operations just to link the gauges of the pruned predictor, thus saving costs considerably, a critical issue in developing countries.

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Notes

  1. In [8] it is also pointed out that, among neuroscientists, such an approach is currently contradicted by “constructivism” [11], which describes the brain development as an increase, rather than a decrease, in the number of synapses. Constructive neural networks algorithms (see for instance [12]), which start with a small network and grow the network until a satisfactory is found, may be interpreted as implementing constructivism in artificial neural networks.

  2. The toolbox is freely available on the Internet: http://www.iau.dtu.dk/research/control/nnsysid.html

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Acknowledegments

The authors thank M. Molari and N. Quaranta, Civil Protection Service of the Lombardy Region, for supplying the data of the Olona river, M. Campolo and A. Soldati, University of Udine, for the data of the Tagliamento river.

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  1. Dipartimento di Elettronica ed Informazione, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy

    Giorgio Corani & Giorgio Guariso

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  1. Giorgio Corani
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  2. Giorgio Guariso
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Correspondence to Giorgio Corani.

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Corani, G., Guariso, G. An application of pruning in the design of neural networks for real time flood forecasting. Neural Comput & Applic 14, 66–77 (2005). https://doi.org/10.1007/s00521-004-0450-z

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