Artificial neural networks applied to flow prediction scenarios in Tomebamba River-Paute watershed, for flood and water quality control and management at City of Cuenca Ecuador

The main aim of this research is to create a model of Artificial Neural Networks (ANN) that allows predicting the flow in Tomebamba River both, at real time and in a certain day of year. As inputs we are using information of rainfall and flow of the stations along of the river. This information is organized in scenarios 1 and each scenario is prepared to a specific area. The information is acquired from the hydrological stations placed in the watershed using an electronic system developed at real time and it supports any kind or brands of this type of sensors. The prediction works very good three days in advance This research includes two ANN models: Back propagation and a hybrid model between back propagation and OWO-HWO. These last two models have been tested in a preliminary research. To validate the results we are using some error indicators such as: MSE, RMSE, EF, CD and BIAS. 1. Resumen La aplicación de modelos matemáticos en el manejo de cuencas hidrográficas tienen requerimientos exigentes de información y en su mayoría no han sido desarrollados para ser aplicados en regiones de montaña. Por esta razón es necesario buscar e implementar modelos que no tengan estos requerimientos y que permitan establecer relaciones entre los datos de entrada y los de salida en una cuenca hidrográfica. Técnicas informáticas de inteligencia artificial permiten establecer relaciones entre los datos de entrada y los de salida en una cuenca hidrográfica. 1 Scenario: Set of information (rainfall and flow) in a specific resolution and order.