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News/Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics
arxiv
PublishedJune 16, 2026 at 4:00 AM
—neutral

Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics

Source
arxiv.orgfull article ↗
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Publisher summary· verbatim

arXiv:2510.02605v2 Announce Type: replace Abstract: While many modern studies are dedicated to ML-based large-sample hydrologic modeling, these efforts have not necessarily translated into predictive improvements that are grounded in enhanced physical-conceptual understanding. Here, we report on a C

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Discussion
Mentioned models
02
  • 01
    Mass-Conserving Perceptron (MCP)
  • 02
    Long Short-Term Memory (LSTM) network
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Tags
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#machine learning#hydrology#modeling#research

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Mentioned models
02
  • 01
    Mass-Conserving Perceptron (MCP)
  • 02
    Long Short-Term Memory (LSTM) network
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine learning#hydrology#modeling#research

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