SOUSA, W. S.; http://lattes.cnpq.br/2702592671280843; SOUSA, Wanderson dos Santos.
Resumen:
The streamflow forecasting in a water system is one of the techniques used to
minimize the impact of the uncertainties of the climate on the administration of the water
resources. That technique can be considered one of the principal challenges related to the
integrated knowledge of the climatology and of the hydrology of the river basin. The aim
of this work was it of modeling the no-lineal relationship between rainfall and streamflow
in the Pianco river basin, in the paraibano semiarid, using the technique of Artificial Neural
Networks (ANN). Here the capacity of ANN was evaluated to model the process rainfallrunoff
in monthly base. It was considered, during the training of ANN, the network
architecture and, weights initialization influence. In the end of the training it was chosen
the best architecture, to model the streamflow monthly mean in the studied basin, with base
in the acting of the model. The architecture of ANN that produced better result was
RC315L with values for the determination coefficient, efficiency coefficient and standard
estimate error (SEE) equal to 92.0%, 77.0% and 8.29 respectively.