A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models. (arXiv:2206.08523v2 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2206.08523

Computational simulations of wildfire spread typically employ empirical
rate-of-spread calculations under various conditions (such as terrain, fuel
type, weather). Small perturbations in conditions can often lead to significant
changes in fire spread (such as speed and direction), necessitating a
computationally expensive large set of simulations to quantify uncertainty.
Model emulation seeks alternative representations of physical models using
machine learning, aiming to provide more efficient and/or simplified surrogate
models. We propose a dedicated spatio-temporal neural network based framework
for model emulation, …

arxiv forecasting lg models network neural neural network temporal