HESS Opinions: Never train an LSTM on a single basin
Frederik Kratzert, Martin Gauch, Daniel Klotz, Grey Nearing
Abstract
Abstract. Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, certain types of time series modeling strategies are popular for rainfall–runoff modeling. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particular that is common: training deep learning models on small, homogeneous data sets (i.e., data from one or a small number of watersheds). In this position paper, we show that Long Short Term Memory (LSTM) streamflow models are best when trained with a large amount of hydrologically diverse data.
Topics & Concepts
MistakeLong short term memoryComputer scienceHomogeneousStreamflowSurface runoffArtificial intelligencePosition (finance)Machine learningDrainage basinGeographyCartographyArtificial neural networkMathematicsRecurrent neural networkEcologyCombinatoricsLawPolitical scienceBiologyEconomicsFinanceHydrology and Watershed Management StudiesHydrological Forecasting Using AIFlood Risk Assessment and Management