FORECAST FOR MEAN MONTHLY DISCHARGE OF THE CAPLINA RIVER, BY APPLYING ARTIFICIAL NEURAL NETWORK (RNA) AND PERIODIC AUTOREGRESSIVE MODEL PAR (1)

Abstract

Caplina river is the main tributary of the hydrographic basin of the same name, It has an extension of 4 239,09 km2, and because of this reason, it is one of the principal sources of water supply for different uses in Tacna’s city. As a result, diverse entities have been interested in learning about the water current and future availability of the river Caplina, because this is key to plan and manage water resource systems. The stochastic models have been present during a long time, the most common alternative in the prediction of flows. Nowadays, the tools of intelligent computation like the artificial neural networks, specially the networks multi-geld with algorithm of retro-spread. In this context, the current research centers its efforts on the application of the neural networks to the prediction of the average monthly flows of the river Caplinastation Bocatoma Calientes, a model developed from neural networks based on information about flows, rainfall and evaporation, as well as opposite to stochastic models. So, 10 models of artificial neural networks were developed with different architectures, and tried with the first subset of information corresponding to the period 1939 - 1999, and then validated with the second subset of information of the period 2000 - 2006. The models of artificial neural networks showed comparatively better performance as for prediction opposite to a periodic autoregressive model of the first order PAR (1).