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A Geometry-Informed Neural Operator surrogate of LISFLOOD-FP for rapid pluvial-fluvial flood forecasting: the case of Forlì, Italy

Nov 2, 2026 Source ↗

Key Insights

Physically based two-dimensional hydraulic models are too slow for real-time inundation forecasting and large ensembles.
We present a time-dependent geometry-informed neural operator (GINO) surrogate.

Cite this synthesis DOI: 10.48550/arXiv.2611.03381 ↗

Show formatted citation

@misc{ acaciadata-research-a-geometry-informed-neural-operator-surrogate-of-lisflood-fp,
  title = { A Geometry-Informed Neural Operator surrogate of LISFLOOD-FP for rapid pluvial-fluvial flood forecasting: the case of Forlì, Italy },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2611.03381 },
  url = { http://arxiv.org/abs/2611.03381v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - A Geometry-Informed Neural Operator surrogate of LISFLOOD-FP for rapid pluvial-fluvial flood forecasting: the case of Forlì, Italy
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2611.03381
UR  - http://arxiv.org/abs/2611.03381v1
ER  -

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Overview

Physically based two-dimensional hydraulic models are too slow for real-time inundation forecasting and large ensembles. We present a time-dependent geometry-informed neural operator (GINO) surrogate

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Stream Processing: A theme covered by this item within the data engineering context.

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Article Metadata

Evergreen Synthesized

stream-processing dataops machine-learning simulation streaming

Stream Processing DataOps

SQI 0.750