Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

Artificial intelligence has been applied in wildfire science and management since the 1990s,
with early applications including neural networks and expert systems. Since then, the field …

Predicting wildfire spread is critical for land management and disaster preparedness. To this
end, we present “Next Day Wildfire Spread,” a curated, large-scale, multivariate dataset of …

While the wildland–urban interface (WUI) is not a new concept, fires in WUI communities
have rapidly expanded in frequency and severity over the past few decades. The number of …

Natural disturbances play a key role in ecosystem dynamics and are important factors for
sustainable forest ecosystem management. Quantitative models are frequently employed to …

Wildfires are part of the natural cycle of life in vegetated regions. The apparent increase in
size and frequency of recent years reflects land management legacy, expansion of human …

Fire is pivotal to the functioning of ecosystems in Australia, affecting the distribution and
abundance of the continent's unique and highly diverse range of plants and animals …

Risk analysis evolved out of the need to make decisions concerning highly stochastic
events, and is well suited to analyse the timing, location and potential effects of wildfires …

Wildland fire management is subject to manifold sources of uncertainty. Beyond the
unpredictability of wildfire behavior, uncertainty stems from inaccurate/missing data, limited …

Despite increasing concern about wildland fire risk in Canada, there is little synthesis of
knowledge that could contribute to the development of a comprehensive risk framework for a …