2026-06-03原文2 min readUpdated: 2026-06-03

Evaluating Transformer and LSTM Frameworks for Prediction in Ungauged Basins

A new study compares encoder-only Transformer and LSTM for upstream streamflow inference in ungauged basins using NOAA National Water Model simulations. LSTM outperformed Transformer overall, and incorporating downstream information boosted median NNSE by over 60%. The findings highlight the importance of architectural inductive bias.

SourcearXiv AIAuthor: Taye Akinrele, James Halgren, Noorbakhsh Amiri Golilarz, Sudip Mittal, Shahram Rahimi

[2606.02791] Evaluating Transformer and LSTM Frameworks for Prediction in Ungauged Basins

[Submitted on 1 Jun 2026]

Title:Evaluating Transformer and LSTM Frameworks for Prediction in Ungauged Basins

View a PDF of the paper titled Evaluating Transformer and LSTM Frameworks for Prediction in Ungauged Basins, by Taye Akinrele and 4 other authors

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Abstract:Watershed networks exhibit convergent topologies in which multiple tributaries merge into downstream channels,integrating diverse upstream hydrological processes. In ungauged basins, the absence of direct observations increases uncertainty and limits the ability to anticipate extreme events. This study evaluates whether an encoder-only Transformer provides an advantage over an LSTM for upstream streamflow inference under limited hydrologic information, using retrospective simulations from the NOAA National Water Model (NWM). Across both upstream-only and combined configurations, the LSTM showed stronger overall performance than the Transformer model across the two configurations. Incorporating downstream information further boosted performance for all models, increasing median NNSE by more than 60%. Rather than treating this as a leaderboard-style comparison, we interpret the experiments as a test of architectural inductive bias for hydrologic sequence inference. The results indicate that recurrent memory remains better aligned with this upstream reconstruction task than an encoder-only Transformer, while downstream hydrologic context provides a strong auxiliary constraint that substantially improves prediction skill across architectures

Comments: 5 pages

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2606.02791 [cs.AI]

(or arXiv:2606.02791v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.02791

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Taye Akinrele [view email] [v1] Mon, 1 Jun 2026 18:57:20 UTC (2,516 KB)

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