Reconstruction of incomplete wildfire data using deep generative models

We present our submission to the Extreme Value Analysis 2021 Data Challenge in which teams were asked to accurately predict distributions of wildfire frequency and size within spatio-temporal regions of missing data. For this competition, we developed a variant of the powerful variational autoencoder models, which we call Conditional Missing data Importance-Weighted Autoencoder (CMIWAE). Our deep latent variable generative model requires little to no feature engineering and does not necessarily rely on the specifics of scoring in the Data Challenge. It is fully trained on incomplete data, with the single objective to maximize log-likelihood of the observed wildfire information. We mitigate the effects of the relatively low number of training samples by stochastic sampling from a variational latent variable distribution, as well as by ensembling a set of CMIWAE models trained and validated on different splits of the provided data.

Automated summary

We submitted our solution to the Extreme Value Analysis 2021 Data Challenge, aiming to accurately predict distributions of wildfire frequency and size in missing spatio-temporal regions. To achieve this, we developed a variant of variational autoencoder models called Conditional Missing data Importance-Weighted Autoencoder (CMIWAE). Our deep latent variable generative model requires minimal feature engineering and is not tied to the specific scoring in the Data Challenge. It is trained on incomplete data, with the sole objective of maximizing the log-likelihood of observed wildfire information. We alleviate the impact of the relatively low number of training samples by employing stochastic sampling from a variational latent variable distribution and ensembling CMIWAE models trained and validated on different data splits.