English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Intense wildfires impact nature, humans, and society, causing catastrophicdamage to property and the ecosystem, as well as the loss of life. Forecastingwildfire front propagation is essential in order to support fire fightingefforts and plan evacuations. The level set method has been widely used toanalyze the change in surfaces, shapes, and boundaries. In particular, a signeddistance function used in level set methods can readily be interpreted torepresent complicated boundaries and their changes in time. While there issubstantial literature on the level set method in wildfire applications, theseimplementations have relied on a heavily-parameterized formula for the rate ofspread. These implementations have not typically considered uncertaintyquantification or incorporated data-driven learning. Here, we present aBayesian spatio-temporal dynamic model based on level sets, which can beutilized for forecasting the boundary of interest in the presence of uncertaindata and lack of knowledge about the boundary velocity. The methodology relieson both a mechanistically-motivated dynamic model for level sets and astochastic spatio-temporal dynamic model for the front velocity. We show theeffectiveness of our method via simulation and with forecasting the fire frontboundary evolution of two classic California megafires - the 2017-2018 Thomasfire and the 2017 Haypress.

A Bayesian Spatio-Temporal Level Set Dynamic Model and Application to Fire Front Propagation