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The potentialities of artificial neural networks are studied as applied to estimating key model parameters required for calculating river runoff by SWAP model in the case of ungauged watersheds. The examined geographic objects were 323 experimental watersheds of MOPEX project. The quality of model parameter estimates based on ANNs with different architecture was analyzed.
The efficiency of the methods of spatial proximity and geostatistics, as well as physico-geographic similarity, is studied as applied to the evaluation of the key model parameters of ungauged watersheds to be used in river runoff calculation by SWAP model. The target geographic objects were 323 experimental watersheds of MOPEX project. The quality of model parameter estimates and reproduction of river...
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