A Deep Learning Architecture for Conservative Dynamical Systems: Application to Rainfall-Runoff Modeling
Abstract
The most accurate and generalizable rainfall-runoff models produced by the hydrological sciences
community to-date are based on deep learning, and in particular, on Long Short Term Memory networks (LSTMs). Although LSTMs have an explicit state space and gates that mimic input-state-output relationships, these models are not based on physical principles. We propose a deep learning architecture that is based on the LSTM and obeys conservation principles. The model is benchmarked on the mass-conservation problem of simulating streamflow
community to-date are based on deep learning, and in particular, on Long Short Term Memory networks (LSTMs). Although LSTMs have an explicit state space and gates that mimic input-state-output relationships, these models are not based on physical principles. We propose a deep learning architecture that is based on the LSTM and obeys conservation principles. The model is benchmarked on the mass-conservation problem of simulating streamflow
Research Areas
