ISSN:
1573-1650
Keywords:
hydrology
;
rainfall-runoff models
;
neural networks
Source:
Springer Online Journal Archives 1860-2000
Topics:
Architecture, Civil Engineering, Surveying
,
Geography
Notes:
Abstract To obtain river flow data, a neural network (NN) is developed and applied to rainfall-runoff transformation. The NN has been built considering a hidden two layer net and the sigmoidal has been used as a response function. Training is conducted using a back-propagation learning rule. In the input layer, both areal and point data values may be considered. The capability to provide a suitable forecast of river runoff has been examined for the Araxisi watershed in Sardinia. Experiments have been made dividing the total extension of observed data into three ten-year periods, assuming each as a training set, learning the NN and simulating the other two decades over the same period. The obtained model efficiency confirms the capability of this approach to supplying a useful tool in the evaluation of rainfall-runoff transformations.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00872489
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