Transformer neural networks for interpretable flood forecasting

Floods are one of the most devastating natural hazards, causing several deaths and conspicuous damages all over the world. In this work, we explore the applicability of the Transformer neural network to the task of flood forecasting. Our goal consists in predicting the water level of a river one day ahead, by using the past water levels of its upstream branches as predictors. The methodology was validated on the severe flood that affected Southeast Europe in May 2014. The results show that the Transformer outperforms recurrent neural networks by more than 4% in terms of the Root Mean Squared Error (RMSE) and 7% in terms of the Mean Absolute Error (MAE). Furthermore, the Transformer requires lower computational costs with respect to recurrent networks. The forecasting errors obtained are considered acceptable according to the domain standards, demonstrating the applicability of the Transformer to the task of flood forecasting.