Gradient boosting for socio-economic wildfire risk assessment

Wildfires are devastating events destroying large parts of physical assets exposed to them in many regions of the world. Therefore, a high-resolution hazard model is needed to accurately assess socio-economic impacts caused by wildfires. Moreover, a probabilistic representation of the hazard covering the range and likelihood of possible wildfire events under certain conditions allows for a more comprehensive risk assessment. This is crucial for many applications, among others the prioritization of adaptation measures and the pricing of insurance.

We determine burning probabilities based on MODIS hotspots and a set of predictors (weather variables, geography, land use) by using a country-specific machine learning model based on the efficient tree boosting system XGBoost. Subsequently, stochastic wildfire events are generated on the basis of these burning probabilities.

Lastly, the open-source climate risk assessment platform CLIMADA is used to compute socio-economic impacts as the combination of the newly developed hazard, an exposure and a vulnerability. The used exposure LitPop spatially distributes macroeconomic indicators (e.g. produced capital) as a function of night light intensity and population density. The vulnerability is represented by an impact function that was calibrated on historic fire damage data. Combining the stochastic impacts with their respective probabilities results in a globally consistent country-specific model of wildfire risk to physical assets.