Exploring Variable Synergy in Multi-Task Deep Learning for Hydrological Modeling
Wenyu Ouyang,
Xuezhi Gu,
Lei Ye
et al.
Abstract:Despite advances in hydrological Deep Learning (DL) models using Single
Task Learning (STL), the intricate relationships among multiple
hydrological components and model inputs might not be comprehensively
encapsulated. This study employed a Long Short-Term Memory (LSTM) neural
network and the CAMELS dataset to develop a Multi-Task Learning (MTL)
model, predicting streamflow and evapotranspiration across multiple
basins. An optimal multi-task loss weight ratio was determined manually
during the validation phas… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.