Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Environmental Research Letters

DOI: 10.1088/1748-9326/ad6fb7

Authors: Kshitij Tayal, Arvind Renganathan, Dan Lu

Institutions: Oak Ridge National Laboratory, University of Minnesota

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Clean water and sanitation

Open Access status: gold

Estimated APC paid: $2,820