Toward Routing River Water in Land Surface Models with Recurrent Neural Networks

arXiv:2404.14212v1 Announce Type: cross
Abstract: Machine learning is playing an increasing role in hydrology, supplementing or replacing physics-based models. One notable example is the use of recurrent neural networks (RNNs) for forecasting streamflow given observed precipitation and geographic characteristics. Training of such a model over the continental United States has demonstrated that a single set of model parameters can be used across independent catchments, and that RNNs can outperform physics-based models. In this work, we take a next step and …

abstract arxiv continental cs.lg example forecasting hydrology machine machine learning networks neural networks physics physics.comp-ph playing precipitation recurrent neural networks role routing surface training type united water