Daily river level forecast based on the development of an artificial neural network: Case study in La Virginia - Risaralda

Abstract

The municipality of La Virginia (Risaralda, Colombia) is constantly affected by fl oods that originate from increased water levels in the Cauca River. Disaster relief agencies do not currently have adequate monitoring systems to identify potential overfl ow events in time-series observations to prevent fl ood damage to homes or injury to the general population. In this paper, various simulation models are proposed for the prediction of fl ooding 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 artifi cial 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 fl ood risks in developing countries.