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 phase for all 591 selected basins with streamflow
data-gaps under 5%. During test period, MTL showed median
Nash-Sutcliffe Efficiency predictions for streamflow and
evapotranspiration at 0.69 and 0.92, consistent with two STL models. The
MTL’s strength appeared when predicting the non-target variable, surface
soil moisture, using probes derived from LSTM cell
states—representative of the internal DL model workings. This
prediction showed a median correlation coefficient of 0.90, surpassing
the 0.88 and 0.89 achieved by the streamflow and evapotranspiration STL
models, respectively. This outcome suggests that MTL models could reveal
additional rules aligned with hydrological processes through the
inherent correlations among multiple hydrological variables, thereby
enhancing their reliability. We termed this as “variable synergy,”
where MTL can simultaneously predict varied targets with comparable STL
performance, augmented by its robust internal representation. Harnessing
this, MTL promises enhanced predictions for high-cost observational
variables and a comprehensive hydrological model.