Pronóstico de caudales medios mensuales del rio caplina, aplicando redes neuronales artificiales (rna) y modelo autorregresivo periódico de primer orden par (1)

• Autor: Vargas Edwin y Cols., Pino
• Formato: artículo
• Fecha de Publicación: 2019
• Institución: Centro de Preparación para la Ciencia y Tecnología
• Repositorio: ECIPERÚ
• Lenguaje: español
• OAI Identifier: oai:revistas.eciperu.net:article/217
• Enlace del recurso: https://revistas.eciperu.net/index.php/ECIPERU/article/view/217
• Nivel de acceso: acceso abierto
• Materia: Cuenca Caplina, Redes Neuronales Artificiales, Series de Tiempo.; Caplina Basin, artificial neural networks, Series of Time.

Caplina river is the main tributary of the hydrographic basin of the same name, It has an extension of 4 239,09 km2, because of this reason it is one of the principal sources of water supply for different uses in Tacna's city. For this reason diverse entities have been interested in knowing the water current and future availability of the river Caplina, because know the above mentioned values performs is the fundamental importance for the planning and managing of the systems of water resources. The stochastic models have been during 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 you multi-geld with algorithm of retro-spread. In this context, the current investigation center his efforts on the application of the neural networks to the prediction of the average monthly flows of the river Caplina-station Bocatoma Calientes, model development of neural networks from information of flows, rainfall and evaporation, as well as the evaluation of the capacity of performance opposite to stochastic models. So, 10 models of artificial neural networks were developed with different architectures, which training was realize with the first subset of information corresponding to the period 1939 - 1999, and his validation 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).