Using an Artificial Neural Network for Hidrological Warnings on the Claro River in Caraguatatuba, São Paulo State

Abstract

The river flow prediction of a hydrological basin with natural disaster risk, such as floods and flash floods, is an important feature of early warning programs. This work presents an approach based on the Artificial Neural Networks (ANNs) to predict (neuroforecast) the flow of the Claro River in Caraguatatuba-SP. The observed data of this hydrological basin were used to perform the training, test and validation of the neural networks. The ANN inputs are constituted by n past observed precipitation data and n-1 observed flow data. However, the output of the ANN is composed by n-ith calculated flow data. The choice of the input number (the quantity of past observed data) was made taking into account the following metrics: the NASH coefficient, which is calculated on the temporal data of the network response; and a set of indexes related to the providing an early warning when the estimated flow exceeds a critical flow. Based on performance metrics, the chosen ANN has a good adjustment to the observed flow data (NASH = 0.77) and good ability for providing an early warnings (efficiency of 0.91).