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Autori principali: Akinade, Basit A., Amanambu, Amobichukwu C., Davis, M. A. Lisa
Natura: Recurso digital
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Pubblicazione: Zenodo 2026
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Accesso online:https://doi.org/10.5281/zenodo.20389784
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author Akinade, Basit A.
Amanambu, Amobichukwu C.
Davis, M. A. Lisa
author_facet Akinade, Basit A.
Amanambu, Amobichukwu C.
Davis, M. A. Lisa
contents Reproducible Python codebase for a basin-by-basin, multi-sensor flood detection network design framework. Implements greedy submodular optimization for sensor placement across HUC10 watersheds, USGS gage integration analysis (Sentinel / Cascade Sentinel / Gap-Filler / Validator classification), and validation against NOAA National Water Model Retrospective v3.0 streamflow records.
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_20389784
institution Zenodo
language
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle Where to Watch the Water: Multi-Sensor Network Design Optimization for Inland Flood Detection
Akinade, Basit A.
Amanambu, Amobichukwu C.
Davis, M. A. Lisa
flood detection
early warning
sensor network design
greedy submodular optimization
HUC10 watersheds
National Water Model
USGS streamgages
hydrometric network
hydrology
water resources
Reproducible Python codebase for a basin-by-basin, multi-sensor flood detection network design framework. Implements greedy submodular optimization for sensor placement across HUC10 watersheds, USGS gage integration analysis (Sentinel / Cascade Sentinel / Gap-Filler / Validator classification), and validation against NOAA National Water Model Retrospective v3.0 streamflow records.
title Where to Watch the Water: Multi-Sensor Network Design Optimization for Inland Flood Detection
topic flood detection
early warning
sensor network design
greedy submodular optimization
HUC10 watersheds
National Water Model
USGS streamgages
hydrometric network
hydrology
water resources
url https://doi.org/10.5281/zenodo.20389784