Machine learning for a heterogeneous water modeling framework

We explore deep learning for the Next Generation Water Resources Modeling Framework (NextGen). We present results from Random forest-based multi-model ensembles, Long Short-Term Memory (LSTM) and differentiable parameter learning hydrological models (δ conceptual models) and attribute sensitivity for ungauged basins. This poster was presented at the CIROH Developers Conference May 29 – June 1, 2024, at the University of Utah in Salt Lake City.