A simple and efficient rainfall-runoff model based on supervised brain emotional learning

To achieve a robust data-driven flood forecasting model, features such as fast learning, appropriate training using insufficient data and reliable prediction of flood flows are of essential importance. These models also have notable vulnerabilities such as decreased accuracy in forecasting peak discharges, challenging simulation of rainy events, performance deterioration in confronting with inadequate training data and weakness due to reduced number of training epochs. In this paper, the supervised brain emotional learning (SBEL) neural network has been used in daily rainfall-runoff modeling of the Dez Dam watershed in the southwest of Iran, as its first application in the field of hydrology. SBEL is a supervised neurocomputing model inspired by the limbic system in the mammalian brain. To create the right responses, the SBEL models the processing of emotional stimuli and the inhibitory mechanism of incorrect responses to stimuli in the emotional brain. The performance of SBEL was compared to the well-known multilayer perceptron (MLP) with 15-8-1 architecture, through different perspectives. The SBEL outperforms MLP in peak flow prediction, limiting the training epochs, reducing the training samples and predictions of rainy events, while improving the mean relative error by 21%, 59.4%, 74.5% and 14.4%, respectively. By placing reduced training data in dry, normal and wet periods, it has been observed that SBEL has more generalization ability in all flow regimes. Overall, the use of this type of emotional intelligence-based model can be of particular interest in developing reliable early flood forecasting and warning systems.