The computational cost of predicting wildland fire spread across large, diverse landscapes is
significant using current models, which limits the ability to use simulations to develop …

A stochastic model motivated by the Lagrangian transported probability density function
method for turbulent reacting flows and the cellular automata approach for forest fires was …

The availability of wildland fire propagation models with parameters estimated in an
accurate way starting from measurements of fire fronts is crucial to predict the evolution of …

Wildfires and their emissions reduce air quality in many regions of the world, contributing to
thousands of premature deaths each year. Smoke forecasting systems have the potential to …

A learning-based wildfire spread model was developed in this study to predict short-term
wildfire spread. Real-time rate of spread (RoS) measurement was first conducted by …

As bias and uncertainties inevitably exist on both wildland fire model states and parameters,
fire simulations do not always accurately forecast the temporal and spatial progression of …

Data-driven techniques are increasingly being applied to complement physics-based
models in fire science. However, the lack of sufficiently large datasets continues to hinder …

Background: Asynchronous observations refer to observations that are obtained at multiple
moments. The observation moments of fire fronts may differ throughout an entire wildfire …

In this paper, we propose rescue ensemble to simulate the dynamic rescue process
between forest fire spread and forest fire rescue, while simultaneously formulating this …

Many nonlinear phenomena, whose numerical simulation is not straightforward, depend on
a set of parameters in a way which is not easy to predict beforehand. Wildland fires in …