| _version_ | 1866902239023464448 |
|---|---|
| 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 |