A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes
DOI10.1007/s10687-022-00460-8arXiv2112.14920WikidataQ117820520 ScholiaQ117820520MaRDI QIDQ6100557
Daniela Cisneros, Raphaël Huser, Yan Gong, Arnab Hazra, Rishikesh Yadav
Publication date: 12 May 2023
Published in: Extremes (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2112.14920
stochastic partial differential equationrandom forestsapproximate Bayesian inferenceGaussian Markov random fieldextreme wildfire frequencies and sizes
Directional data; spatial statistics (62H11) Linear regression; mixed models (62J05) Applications of statistics to environmental and related topics (62P12) Generalized linear models (logistic models) (62J12) Statistics of extreme values; tail inference (62G32)
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