A marginal modelling approach for predicting wildfire extremes across the contiguous United States.
- Published in:
- Extremes, 2023, v. 26, n. 2, p. 381, doi. 10.1007/s10687-023-00469-7
- By:
- Publication type:
- Article
Works matching IS 13861999 AND DT 2023 AND VI 26 AND IP 2
Results: 8
1
2
Editorial: EVA 2021 data challenge on spatiotemporal prediction of wildfire extremes in the USA.
- Published in:
- 2023
- By:
- Publication type:
- Editorial
3
Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach.
- Published in:
- Extremes, 2023, v. 26, n. 2, p. 339, doi. 10.1007/s10687-023-00463-z
- By:
- Publication type:
- Article
4
Analysis of wildfires and their extremes via spatial quantile autoregressive model.
- Published in:
- Extremes, 2023, v. 26, n. 2, p. 353, doi. 10.1007/s10687-023-00462-0
- By:
- Publication type:
- Article
5
A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes.
- Published in:
- Extremes, 2023, v. 26, n. 2, p. 301, doi. 10.1007/s10687-022-00460-8
- By:
- Publication type:
- Article
6
Reconstruction of incomplete wildfire data using deep generative models.
- Published in:
- Extremes, 2023, v. 26, n. 2, p. 251, doi. 10.1007/s10687-022-00459-1
- By:
- Publication type:
- Article
7
Simple random forest classification algorithms for predicting occurrences and sizes of wildfires.
- Published in:
- Extremes, 2023, v. 26, n. 2, p. 331, doi. 10.1007/s10687-022-00458-2
- By:
- Publication type:
- Article
8
Gradient boosting with extreme-value theory for wildfire prediction.
- Published in:
- Extremes, 2023, v. 26, n. 2, p. 273, doi. 10.1007/s10687-022-00454-6
- By:
- Publication type:
- Article