Artificial neural networks applied to flow prediction: A use case for the Tomebamba river

Abstract

The main aim of this research is to create a model based on Artificial Neural Networks (ANN) that allows predicting the flow in Tomebamba river, at real time and in a specific day of a year. As inputs, this research is using information of rainfall and flow of the stations along of the river. This information is organized in scenarios and each scenario is prepared to a specific area. For this article, we have selected two scenarios. 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: Backpropagation and a hybrid model between back propagation and OWO-HWO (output weight optimization–hidden weight optimization) to select the initial weights of the connection. 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. The results of this research reached high levels of reliability and the level of error is minimal. These predictions are useful to avoid floods in the city of Cuenca in Ecuador.