  This paper describes the methodology used by the team RedSea in the data
competition organized for EVA 2021 conference. We develop a novel two-part
model to jointly describe the wildfire count data and burnt area data provided
by the competition organizers with covariates. Our proposed methodology relies
on the integrated nested Laplace approximation combined with the stochastic
partial differential equation (INLA-SPDE) approach. In the first part, a binary
non-stationary spatio-temporal model is used to describe the underlying process
that determines whether or not there is wildfire at a specific time and
location. In the second part, we consider a non-stationary model that is based
on log-Gaussian Cox processes for positive wildfire count data, and a
non-stationary log-Gaussian model for positive burnt area data. Dependence
between the positive count data and positive burnt area data is captured by a
shared spatio-temporal random effect. Our two-part modeling approach performs
well in terms of the prediction score criterion chosen by the data competition
organizers. Moreover, our model results show that surface pressure is the most
influential driver for the occurrence of a wildfire, whilst surface net solar
radiation and surface pressure are the key drivers for large numbers of
wildfires, and temperature and evaporation are the key drivers of large burnt
areas.
