Articles | Volume 30, issue 10
https://doi.org/10.5194/hess-30-3165-2026
https://doi.org/10.5194/hess-30-3165-2026
Research article
 | 
22 May 2026
Research article |  | 22 May 2026

A hybrid Kolmogorov–Arnold Networks-based model with attention for predicting Arctic river streamflow

Renjie Zhou and Shiqi Liu

Data sets

Zhou-R/HESS_KAN: v0.01 Renjie Zhou https://doi.org/10.5281/zenodo.19862397

Model code and software

Zhou-R/HESS_KAN: v0.01 Renjie Zhou https://doi.org/10.5281/zenodo.19862397

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Short summary
Arctic rivers move enormous amounts of water and carbon into the ocean, influencing global climate, but their flow is hard to predict because the region is remote and the frozen ground behaves in unusual ways. This research combines artificial intelligence with the physics of snow and permafrost to forecast river flow more accurately. Demonstrated on the Kolyma River, the new model outperforms existing approaches and provides a robust framework for understanding Arctic hydrological systems.
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