Pages that link to "Item:Q2686054"
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The following pages link to Spatiotemporal wildfire modeling through point processes with moderate and extreme marks (Q2686054):
Displaying 9 items.
- Multifractal point processes and the spatial distribution of wildfires in French Mediterranean regions (Q2066243) (← links)
- Birth-jump processes and application to forest fire spotting (Q3304600) (← links)
- Editorial: EVA 2021 data challenge on spatiotemporal prediction of wildfire extremes in the USA (Q6100553) (← links)
- Gradient boosting with extreme-value theory for wildfire prediction (Q6100555) (← links)
- A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes (Q6100557) (← links)
- A marginal modelling approach for predicting wildfire extremes across the contiguous United States (Q6100565) (← links)
- Data-driven chimney fire risk prediction using machine learning and point process tools (Q6138624) (← links)
- An efficient workflow for modelling high-dimensional spatial extremes (Q6581672) (← links)
- Bayesian modeling of insurance claims for hail damage (Q6665497) (← links)
