Investigating the role of saliency analysis with a neural network rainfall-runoff model

Author:

Abrahart Robert J,See Linda,Kneale Pauline E

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Information Systems

Reference13 articles.

1. Abrahart, R.J., 1998. Neural networks and the problem of accumulated error: an embedded solution that offers new opportunities for modelling and testing. In: Babovic, V., Larsen, C.L., (Eds.), Hydroinformatics ’98: Proceedings of the Third International Conference on Hydroinformatics, Copenhagen, Denmark, Vol. 2. Balkema, Rotterdam,pp. 725–731.

2. Abrahart, R.J., See, L., Kneale, P.E., 1998. New tools for neurohydrologists: using ‘network pruning’ and ‘model breeding’ algorithms to discover optimum inputs and architectures. GeoComputation ’98: Proceedings of the Third International Conference on GeoComputation, University of Bristol, GeoComputation CD-ROM, Manchester, ISBN 0-9533477–0–2.

3. Sensitivity analysis of the Systeme Hydrologique Europeen for an upland catchment;Bathurst;Journal of Hydrology,1986

4. Testing a physically-based flood forecasting model (TOPMODEL) for three U.K. catchments;Beven;Journal of Hydrology,1984

5. Bishop, C.M., Hinton, G., 1995. Neural Networks for Pattern Recognition. Clarendon Press, Oxford, 500pp.

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