HESS Opinions: Never train a Long Short-Term Memory (LSTM) network 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, Long Short-Term Memory (LSTM) networks 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, typically data from only a single hydrological basin. In this position paper, we show that LSTM rainfall–runoff models are best when trained with data from a large number of basins.
Topics & Concepts
Term (time)Long short term memoryComputer scienceStructural basinArtificial intelligenceGeologyRecurrent neural networkArtificial neural networkPaleontologyAstronomyPhysicsHydrological Forecasting Using AIHydrology and Watershed Management StudiesMeteorological Phenomena and Simulations