IgniWise Dataset — Prescribed Burn Window Prediction for Spain (1983–2015)
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| Sprache: | Spanisch |
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2026
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| _version_ | 1866902154394992640 |
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| author | Romera Martínez, Sergio |
| author_facet | Romera Martínez, Sergio |
| contents | <p>Machine learning dataset for predicting safe prescribed burn windows across <br>48 Spanish provinces, supporting the IgniWise open-source system.</p> <p>Dataset contents:<br>- training_data.csv: 11,996 records derived from historical fire occurrence <br> data (MITECO / IEPNB, 1983–2015) with 20 features including meteorological <br> variables (statistically approximated from Spanish climate distributions), <br> FWI indices (Van Wagner, 1987), and real geographic features per province<br>- random_forest_v1.pkl: Trained Random Forest model (200 estimators, <br> 5-fold CV accuracy: 99.5% ± 0.002)<br>- provincias_geo.geojson: Spanish province geometries enriched with real <br> topographic (Copernicus DEM GLO-30), vegetation (Sentinel-2 NDVI via GEE), <br> and land cover features (CORINE Land Cover 2018)</p> <p>Note on meteorological features: weather variables in training_data.csv are <br>statistical approximations calibrated to provincial climate. Operational <br>predictions use real-time data from OpenWeatherMap. The 99.5% CV accuracy <br>reflects model fit to the classification rules rather than independent <br>real-world validation, which requires field data from executed prescribed burns.</p> <p>Sources: MITECO/IEPNB (fire occurrences), Copernicus DEM GLO-30 (topography),<br>Sentinel-2/GEE (NDVI), CORINE Land Cover 2018 (vegetation cover).<br>FWI methodology: Van Wagner, C.E. (1987), Canadian Forest Service.</p> <p>License: CC BY 4.0 | Web: igniwise.com | Code: github.com/TrueRomanZe/igniwise</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19144668 |
| institution | Zenodo |
| language | spa |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | IgniWise Dataset — Prescribed Burn Window Prediction for Spain (1983–2015) Romera Martínez, Sergio Wildfires Machine Learning Spain Prescribed Burns Fire Weather Index Random Forest Wildfire Prediction Fire Management <p>Machine learning dataset for predicting safe prescribed burn windows across <br>48 Spanish provinces, supporting the IgniWise open-source system.</p> <p>Dataset contents:<br>- training_data.csv: 11,996 records derived from historical fire occurrence <br> data (MITECO / IEPNB, 1983–2015) with 20 features including meteorological <br> variables (statistically approximated from Spanish climate distributions), <br> FWI indices (Van Wagner, 1987), and real geographic features per province<br>- random_forest_v1.pkl: Trained Random Forest model (200 estimators, <br> 5-fold CV accuracy: 99.5% ± 0.002)<br>- provincias_geo.geojson: Spanish province geometries enriched with real <br> topographic (Copernicus DEM GLO-30), vegetation (Sentinel-2 NDVI via GEE), <br> and land cover features (CORINE Land Cover 2018)</p> <p>Note on meteorological features: weather variables in training_data.csv are <br>statistical approximations calibrated to provincial climate. Operational <br>predictions use real-time data from OpenWeatherMap. The 99.5% CV accuracy <br>reflects model fit to the classification rules rather than independent <br>real-world validation, which requires field data from executed prescribed burns.</p> <p>Sources: MITECO/IEPNB (fire occurrences), Copernicus DEM GLO-30 (topography),<br>Sentinel-2/GEE (NDVI), CORINE Land Cover 2018 (vegetation cover).<br>FWI methodology: Van Wagner, C.E. (1987), Canadian Forest Service.</p> <p>License: CC BY 4.0 | Web: igniwise.com | Code: github.com/TrueRomanZe/igniwise</p> |
| title | IgniWise Dataset — Prescribed Burn Window Prediction for Spain (1983–2015) |
| topic | Wildfires Machine Learning Spain Prescribed Burns Fire Weather Index Random Forest Wildfire Prediction Fire Management |
| url | https://doi.org/10.5281/zenodo.19144668 |