Replication Study: Enhancing Hydrological Modeling with Physics-Guided Machine Learning

arXiv:2402.13911v1 Announce Type: new
Abstract: Current hydrological modeling methods combine data-driven Machine Learning (ML) algorithms and traditional physics-based models to address their respective limitations incorrect parameter estimates from rigid physics-based models and the neglect of physical process constraints by ML algorithms. Despite the accuracy of ML in outcome prediction, the integration of scientific knowledge is crucial for reliable predictions. This study introduces a Physics Informed Machine Learning (PIML) model, which merges the process understanding of conceptual hydrological models with the …

abstract accuracy algorithms arxiv constraints cs.lg current data data-driven integration limitations machine machine learning ml algorithms modeling physics prediction process replication study type