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Revista Facultad de Ingeniería Universidad de Antioquia

Print version ISSN 0120-6230

Abstract

MORALES-PINZON, Tito; CESPEDES-RESTREPO, Juan David  and  FLOREZ-CALDERON, Manuel Tiberio. Daily river level forecast based on the development of an artificial neural network: case study in La Virginia -Risaralda. Rev.fac.ing.univ. Antioquia [online]. 2015, n.76, pp.46-57. ISSN 0120-6230.  http://dx.doi.org/10.17533/udea.redin.n76a06.

The municipality of La Virginia (Risaralda, Colombia) is constantly affected by floods that originate from increased water levels in the Cauca River. Disaster relief agencies do not currently have adequate monitoring systems to identify potential overflow events in time-series observations to prevent flood damage to homes or injury to the general population. In this paper, various simulation models are proposed for the prediction of flooding that contributes as a technical tool to the development and implementation of early warning systems to improve the responsiveness of disaster relief agencies. The models, which are based on artificial neural networks, take hydroclimatological information from different stations along the Cauca River Basin, and the trend indicates the average daily level of the river within the next 48 hours. This methodology can be easily applied to other urban areas exposed to flood risks in developing countries.

Keywords : Artificial neural networks; flood forecasting; flood risk.

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