Groundwater Level Prediction Using Artificial Neural Network Model

Abstract

Due to its topographical characteristics and its particular hydrometerelogical regime, Colombia has long extensions susceptible of flooding; considering this situation, in this article a system based on artificial neural networks is proposed to model and predict the water level in the river system. The variables used in this model include: historical water level data in different stations, rain season and La Niña or El Niño phenomenon. The results showed a good system performance.