A spatial Poisson hurdle model with application to wildfires

TL;DR

This paper introduces a spatial Poisson hurdle model to analyze wildfire occurrences, capturing geographical variation and key environmental predictors, with applications to Indonesia and Australia for improved disaster management.

Contribution

The paper develops a novel spatial Poisson hurdle model incorporating latent effects for wildfire count data, with efficient empirical Bayes inference for practical application.

Findings

Elevation, tree cover, humidity, and temperature are key predictors.

Opposing effects of temperature and humidity interactions on wildfires.

Model accurately reflects known wildfire patterns.

Abstract

Modelling wildfire occurrences is important for disaster management including prevention, detection and suppression of large catastrophic events. We present a spatial Poisson hurdle model for exploring geographical variation of monthly counts of wildfire occurrences and apply it to Indonesia and Australia. The model includes two a priori independent spatially structured latent effects that account for residual spatial variation in the probability of wildfire occurrence, and the positive count rate given an occurrence. Inference is provided by empirical Bayes using the Laplace approximation to the marginal posterior which provides fast inference for latent Gaussian models with sparse structures. In both cases, our model matched several empirically known facts about wildfires. We conclude that elevation, percentage tree cover, relative humidity, surface temperature, and the interaction between humidity and temperature to be important predictors of monthly counts of wildfire occurrences. Further, our findings show opposing effects for surface temperature and its interaction with relative humidity.