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When high levels of urban development, and erratic patterns of high precipitation combine in a small geographical area, there is a significant increase in the risk of human and/or material losses due to flooding and related incidents. With the objective of providing a method for the estimation of precipitation patterns in an area with a high risk of flooding, the current document describes the design and implementation of a neural-network-based system as a potential solution. With the use of TRMM satellite data, and ground station flow measurements in the Juan Díaz river, two models are developed for the estimation of the behavior of these magnitudes: one for estimating precipitation levels based on time, and one that estimates the flow of the river as a function of precipitation.Keywords: modeling, estimation, precipitation, flow, river.

Análisis y Estimación de Precipitación para Modelado de Caudal del Río Juan Díaz en el Distrito de Panamá Utilizando Redes Neuronales