Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T10:22:06.189828Z
Paper Citation Record · LEDGER
Learning Surrogate Rainfall-driven Inundation Models with Few Data
As of 18 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 2 inbound Pith citation observations for arXiv:2411.19323.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T23:05:26.639126Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
55 of 55 outbound references displayed
- verified exact0
- verified fuzzy48
- unresolved7
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- malformed identifier0
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External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
Observation f06648e6-3ef3-4943-b6ba-66236b3a12d8 · outbound
This paper cites an unresolved cited work.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Unresolved cited work
Reference 1
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T10:22:06.973787Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 56030b91-530f-4c85-8d9d-33359def8d1c · outbound
This paper cites Flood inundation mapping and depth modelling using machine learning algorithms and microwave data.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Flood inundation mapping and depth modelling using machine learning algorithms and microwave data
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.960266Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4bf795a7-ae04-461f-b546-7d5183209052 · outbound
This paper cites Hydrodynamic modeling for flood prediction using data-driven techniques.Water Resources Research, 57(7):e2020WR029821, 2021.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Hydrodynamic modeling for flood prediction using data-driven techniques.Water Resources Research, 57(7):e2020WR029821, 2021
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.946003Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2470bc71-3c3a-4f9e-9649-fadc076be711 · outbound
This paper cites A digital elevation model based method for a rapid estimation of flood inundation depth.Journal of Flood Risk Management, 12:e12541, 2019.
Learning Surrogate Rainfall-driven Inundation Models with Few Data A digital elevation model based method for a rapid estimation of flood inundation depth.Journal of Flood Risk Management, 12:e12541, 2019
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.932084Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a0d9b20b-c404-40d1-a343-6379b8c98c8b · outbound
This paper cites Floodriskas- sessment using machine learning models.Journal of Environmental Management, 293:106899, 2021.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Floodriskas- sessment using machine learning models.Journal of Environmental Management, 293:106899, 2021
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.917027Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d8663f97-667f-4ec6-ac27-88cf7a42c09e · outbound
This paper cites an unresolved cited work.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Unresolved cited work
Reference 6
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T10:22:06.901525Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b1128fb4-f20d-4cbc-b36b-fce575ce92bd · outbound
This paper cites an unresolved cited work.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T10:22:06.887483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 59ee3ca2-dc78-4060-94b7-88556d69e5d5 · outbound
This paper cites López-Lopera, Stéphane Girard, and Jean-Michel Marin.
Learning Surrogate Rainfall-driven Inundation Models with Few Data López-Lopera, Stéphane Girard, and Jean-Michel Marin
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.873541Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3ecf92df-3fdd-41a2-ba18-8846a305498e · outbound
This paper cites Data-efficientmodelsforimprovingfloodprediction.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Data-efficientmodelsforimprovingfloodprediction
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.858961Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation cd457afc-e654-47cc-8ff0-7f6f2ed2e654 · outbound
This paper cites Development of surrogate models for rapid flood forecasting.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Development of surrogate models for rapid flood forecasting
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.844568Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation bbc07d42-c8e2-490c-a35a-4185c29be266 · outbound
This paper cites Deep residual learning for image recognition.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Deep residual learning for image recognition
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T10:22:06.003324Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 78e664c1-3ac6-4aa4-b050-b6d20cd676f0 · outbound
This paper cites Fast ensemble smoothing.Ocean Dynamics, 57(2):123– 134, 2007.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Fast ensemble smoothing.Ocean Dynamics, 57(2):123– 134, 2007
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.822169Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b22118aa-d954-4bc4-98c7-11077cde0cd8 · outbound
This paper cites A realtime ob- servatory for laboratory simulation of planetary flows.Experiments in Fluids, 48(5):915–925, 2010.
Learning Surrogate Rainfall-driven Inundation Models with Few Data A realtime ob- servatory for laboratory simulation of planetary flows.Experiments in Fluids, 48(5):915–925, 2010
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.809005Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 419d4502-f72b-4330-bc4b-68d7d64ddf8b · outbound
This paper cites Informative Neural Ensemble Kalman Learning.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Informative Neural Ensemble Kalman Learning
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T10:22:06.017160Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1ff1535f-e0f9-469d-8d1f-4f757da1d971 · outbound
This paper cites Statistical-physical adversarial learning from data and mod- els for downscaling rainfall extremes.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Statistical-physical adversarial learning from data and mod- els for downscaling rainfall extremes
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.795779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d828e86e-ff71-4c4e-9729-74fb8cd78f02 · outbound
This paper cites Thornton, Steven W.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Thornton, Steven W
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.782203Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 525a4314-f50e-495f-b59b-1bbd8b3c77f2 · outbound
This paper cites Hybrid surrogate models integrating lstm and cnn architectures for flood prediction.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Hybrid surrogate models integrating lstm and cnn architectures for flood prediction
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.768680Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 17a7cf73-c048-43f9-8069-9ec3001bd73e · outbound
This paper cites Real-time urban flood depth mapping: Convolutional neural networks for pluvial and fluvial flood emu- lation.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Real-time urban flood depth mapping: Convolutional neural networks for pluvial and fluvial flood emu- lation
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.755127Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9cacd331-22dd-4b29-8c87-5f32f39187e6 · outbound
This paper cites Behzadan.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Behzadan
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.741134Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0ac4b22e-4b6c-4ad8-a9c6-df1bc33fe283 · outbound
This paper cites Flood hazard mapping using data-driven models and principal component analysis.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Flood hazard mapping using data-driven models and principal component analysis
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.726695Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 381235a9-c37a-4e4b-8643-ae887e2918ac · outbound
This paper cites Flood risk assessment using machine learning models.Journal of Environ- mental Management, 293:106899, 2021.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Flood risk assessment using machine learning models.Journal of Environ- mental Management, 293:106899, 2021
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.713655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ce4d2a07-69dc-44c4-a4c3-00280e263092 · outbound
This paper cites Improving urban flood forecasting using integrated machine learning mod- els.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Improving urban flood forecasting using integrated machine learning mod- els
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.699725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a46d9545-6d8b-40ad-a7fa-b80cc0056ea3 · outbound
This paper cites Zahura and Jonathan L.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Zahura and Jonathan L
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.686465Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 22a32b36-db29-4d37-98d1-2e4b100abadf · outbound
This paper cites Application of machine learning in flood forecasting.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Application of machine learning in flood forecasting
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.673667Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 34cc2985-e136-42f7-8b65-481559209e72 · outbound
This paper cites Flood mapping using machine learning techniques.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Flood mapping using machine learning techniques
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.660255Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e59e9b9d-a03d-4ad6-a4a3-fe9eded0e40b · outbound
This paper cites Learning inter-annual flood loss risk models from historical flood insurance claims.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Learning inter-annual flood loss risk models from historical flood insurance claims
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.646927Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation cdb750df-48b8-4273-8d79-6f39c26b9148 · outbound
This paper cites Enhancing machine learning models through multimodal approaches in global flood prediction.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Enhancing machine learning models through multimodal approaches in global flood prediction
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.633154Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 53518ee3-e3ad-4211-8f79-c4cbb46b3d4e · outbound
This paper cites A comprehensive review of automated flood prediction models.
Learning Surrogate Rainfall-driven Inundation Models with Few Data A comprehensive review of automated flood prediction models
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.619719Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9b88a6b2-8399-445f-afeb-500633305050 · outbound
This paper cites Application of lightgbm in rapid flood prediction.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Application of lightgbm in rapid flood prediction
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.606464Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 34202ce6-f252-4efc-a044-5202f0181806 · outbound
This paper cites Ashiqur Rahman et al.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Ashiqur Rahman et al
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.593056Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ab5f9892-2eb7-45e2-b901-51fefb6319df · outbound
This paper cites an unresolved cited work.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Unresolved cited work
Reference 31
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T10:22:06.580237Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ccb107f0-ec7d-48f8-b600-c8e8a061bafa · outbound
This paper cites Flood-precip gan for synthetic flood event generation.IEEE Transactions on Geoscience and Remote Sensing, 61:1–12, 2023.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Flood-precip gan for synthetic flood event generation.IEEE Transactions on Geoscience and Remote Sensing, 61:1–12, 2023
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.567048Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b6637008-8a92-4770-b614-6f36a49af512 · outbound
This paper cites Raissi, P.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Raissi, P
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.553351Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f1785870-dd0d-4767-a30b-9449dc408c6c · outbound
This paper cites Application of physics-informed neural networks in flood modeling.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Application of physics-informed neural networks in flood modeling
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.540087Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4cdd7cfc-03c0-4058-8e4e-82d2162037af · outbound
This paper cites Importance of interpretability in hydrological models.Hydrology and Earth System Sciences, 27:123–139, 2023.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Importance of interpretability in hydrological models.Hydrology and Earth System Sciences, 27:123–139, 2023
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.526114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d0b183b1-8249-42b2-aa24-3bf4713924a4 · outbound
This paper cites Challenges in deep learning models for flood forecasting.Journal of Hydrology, 615:128893, 2023.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Challenges in deep learning models for flood forecasting.Journal of Hydrology, 615:128893, 2023
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.512782Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 99f56a91-cae8-4fa5-8f97-ba40b8ab0aa3 · outbound
This paper cites Hybrid models combining machine learning and physical models for flood prediction.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Hybrid models combining machine learning and physical models for flood prediction
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.499240Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fe31a97d-191c-4ee8-9aa7-99b76a071500 · outbound
This paper cites an unresolved cited work.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Unresolved cited work
Reference 38
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T10:22:06.485109Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 93298bf4-6090-4977-b4d4-493155d9ba4a · outbound
This paper cites Improving flood predictions using hybrid models in data-scarce regions.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Improving flood predictions using hybrid models in data-scarce regions
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.471714Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 809e035a-a86f-4e96-97c5-b0dc34ece3dc · outbound
This paper cites Probabilistic flood forecasting with lstm networks.Journal of Hydrometeo- rology, 24(2):345–362, 2023.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Probabilistic flood forecasting with lstm networks.Journal of Hydrometeo- rology, 24(2):345–362, 2023
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.458056Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation cbafabac-01ee-470f-8559-b2a29b0ba087 · outbound
This paper cites Surrogate models in flood risk assessment.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Surrogate models in flood risk assessment
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.443728Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ceb4c385-3d2f-427a-b83f-6c06872fce7e · outbound
This paper cites Artificial neural networks for flood modeling.Hydrological Sciences Journal, 65(5):813–825, 2020.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Artificial neural networks for flood modeling.Hydrological Sciences Journal, 65(5):813–825, 2020
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.430200Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1d370beb-f090-4c87-9515-d885a2fe81be · outbound
This paper cites Computationally efficient flood modeling.Environmental Modelling & Software, 107:50–63, 2018.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Computationally efficient flood modeling.Environmental Modelling & Software, 107:50–63, 2018
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.415650Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9d188c42-c5c2-4da4-a402-f6e606215edd · outbound
This paper cites López-Lopera, Déborah Idier, Jérémy Rohmer, and François Bachoc.
Learning Surrogate Rainfall-driven Inundation Models with Few Data López-Lopera, Déborah Idier, Jérémy Rohmer, and François Bachoc
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.402517Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0e00b9a3-6cfc-4968-837d-ccc119c22829 · outbound
This paper cites Rapid flood prediction using encoder-decoder lstm models.Journal of Hy- drology, 614:128787, 2023.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Rapid flood prediction using encoder-decoder lstm models.Journal of Hy- drology, 614:128787, 2023
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.388981Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d68ad8c6-f3c7-4711-a42d-94b3856c3305 · outbound
This paper cites Lisflood-fp 8.1: New gpu accelerated solvers for faster fluvial/pluvial flood simulations.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Lisflood-fp 8.1: New gpu accelerated solvers for faster fluvial/pluvial flood simulations
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.375131Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8062b9c4-a432-4786-bf3b-ed978da1e9c1 · outbound
This paper cites Elsevier, 1992.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Elsevier, 1992
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.361380Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 10ce2d50-5103-47d9-a61a-2af2a6d20816 · outbound
This paper cites Daymet: Daily surface weather data on a 1-km grid for north america, version 2.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Daymet: Daily surface weather data on a 1-km grid for north america, version 2
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.346796Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b3311389-b833-44a9-990e-0f44797727e8 · outbound
This paper cites The shuttle radar topography mission.
Learning Surrogate Rainfall-driven Inundation Models with Few Data The shuttle radar topography mission
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.331889Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f4644c8e-a52d-4d5f-8274-18cb647b880c · outbound
This paper cites The national land cover database.
Learning Surrogate Rainfall-driven Inundation Models with Few Data The national land cover database
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.316805Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2ca559d4-e190-4ec8-91fd-b453c9966173 · outbound
This paper cites National water information system (nwis).ArXiv preprint, 2023.
Learning Surrogate Rainfall-driven Inundation Models with Few Data National water information system (nwis).ArXiv preprint, 2023
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.300865Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 698a5183-d70d-4cdc-be3e-27e63f636984 · outbound
This paper cites Hydrofunctions: A python package for working with hydrology data.ArXiv preprint, 2023.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Hydrofunctions: A python package for working with hydrology data.ArXiv preprint, 2023
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.286680Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 61d440a9-4b99-4a70-8a11-7f57fc848643 · outbound
This paper cites Modeling com- pound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Modeling com- pound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.272399Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation cea906f1-f55e-408d-925d-ab0c4ebbc22f · outbound
This paper cites The climate hazards infrared precipitation with stations a new environmental record for mon- itoring extremes.
Learning Surrogate Rainfall-driven Inundation Models with Few Data The climate hazards infrared precipitation with stations a new environmental record for mon- itoring extremes
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.257445Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 691747dc-5dbb-4f08-95f5-05426ff627ab · outbound
This paper cites Overview of the coupled model intercomparison project phase 6 (cmip6) experimental design and organization.Geoscientific Model Development, 9(5):1937– 1958, 2016.
Learning Surrogate Rainfall-driven Inundation Models with Few Data Overview of the coupled model intercomparison project phase 6 (cmip6) experimental design and organization.Geoscientific Model Development, 9(5):1937– 1958, 2016
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T10:22:06.242608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
Observation 77651715-f138-4324-b551-06628a28cab0 · inbound
LASSE: Learning Active Sampling for Storm Tide Extremes in Non-Stationary Climate Regimes cites this paper.
LASSE: Learning Active Sampling for Storm Tide Extremes in Non-Stationary Climate Regimes Learning Surrogate Rainfall-driven Inundation Models with Few Data
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T23:05:26.639126Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e04f0d44-e2a9-4b01-92f2-42129fe9783d · inbound
Observation-Guided Neural Surrogate Learning for Scientific Simulation Emulation: A Single-Gauge Flood-Inundation Proof of Concept cites this paper.
Observation-Guided Neural Surrogate Learning for Scientific Simulation Emulation: A Single-Gauge Flood-Inundation Proof of Concept Learning Surrogate Rainfall-driven Inundation Models with Few Data
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T03:09:51.411158Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.