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Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data

Journal Article · · Environmental Research Letters
 [1];  [1];  [1]
  1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
+ Show Author Affiliations
Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global long short-term memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, yet acquiring detailed basin attributes remains a challenge. To overcome this, we introduce the Geo-vision transformer (ViT)-LSTM model, a novel approach that enriches LSTM predictions by integrating basin attributes derived from remote sensing with a ViT architecture. Applied to 531 basins across the Contiguous United States, our method demonstrated superior prediction accuracy in both temporal and spatiotemporal extrapolation scenarios. Geo-ViT-LSTM marks a significant advancement in land surface modeling, providing a more comprehensive and effective tool for better understanding the environment responses to climate change.
Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
2439862
Alternate ID(s):
OSTI ID: 2440212
Journal Information:
Environmental Research Letters, Journal Name: Environmental Research Letters Journal Issue: 10 Vol. 19; ISSN 1748-9326
Publisher:
IOP PublishingCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

54 ENVIRONMENTAL SCIENCES
machine learning in hydrology
multimodal data integration
remote sensing
streamflow prediction