A glimpse into the Unobserved: Runoff simulation for ungauged catchments with LSTMs
Sprache des Titels:
Neural Information Processing Systems (NIPS 2018)
Runoff predictions of a river from meteorological inputs is a key task in the field of hydrology. However, current hydrological models require a substantial amount of parameter tuning on basis of historical records. If no historical runoff observations are available it is very challenging to produce good predictions. In this study we explore the capability of LSTMs for simulating the runoff for these ungauged cases. A single LSTM, also including static catchment attributes as input, is trained to learn a general hydrological model from hundreds of catchments throughout the contiguous United States of America and evaluated against catchments not used during training. Our results suggest that LSTMs a) are able to learn a general hydrological model and b) in the majority of catchments outperform an established hydrological model, which was especially trained for these catchments.