 This paper proposes a novel approach to regional rainfall runoff modeling by utilizing long short-term memory networks, LSTMs. This approach was tested against other hydrological models and demonstrated improved performance. Additionally, the authors proposed an adaptation to the standard LSTM architecture called Entity Aware LSTM, ELSTM, which allowed for learning catchment similarities as a feature layer in a deep learning model. These learned catchment similarities corresponded well with prior hydrological understanding. This article was authored by F. Krotzert, D. Klotz, G. Shalev, and others.