Wildfire prediction to inform fire management: statistical science challenges

DOI10.1214/13-STS451zbMath1331.86029arXiv1312.6481OpenAlexW1978816960WikidataQ117555957 ScholiaQ117555957MaRDI QIDQ5965043

Stephen W. Taylor, C. B. Dean, Douglas G. Woolford, David L. Martell

Publication date: 2 March 2016

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1312.6481

zbMATH Keywords

predictionenvironmetricsreviewforest firewildland fire

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12) Geostatistics (86A32)

Related Items (5)

Canadian contributions to environmetrics ⋮ Gradient boosting with extreme-value theory for wildfire prediction ⋮ Simple random forest classification algorithms for predicting occurrences and sizes of wildfires ⋮ Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach ⋮ On the merits of sparse surrogates for global sensitivity analysis of multi-scale nonlinear problems: application to turbulence and fire-spotting model in wildland fire simulators

Uses Software

• Prometheus
• BEHAVE
• ElemStatLearn
• gamair
• FARSITE
• FlamMap

Cites Work

• Unnamed Item
• Prometheus
• Point process modeling of wildfire hazard in Los Angeles county, California
• An elliptical growth model of forest fire fronts and its numerical solution
• Testing Separability in Spatial-Temporal Marked Point Processes
• Reconstructing the history of forest fire frequency: Identifying hazard rate change points using the bayes information criterion

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