Works matching IS 13861999 AND DT 2023 AND VI 26 AND IP 2
Results: 8
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
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- Publication type:
- Article
Editorial: EVA 2021 data challenge on spatiotemporal prediction of wildfire extremes in the USA.
- Published in:
- 2023
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- Publication type:
- Editorial
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
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- Article
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
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- Publication type:
- Article
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
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- Publication type:
- Article
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
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- Publication type:
- Article
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
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- Publication type:
- Article
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
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- Publication type:
- Article