Abstract
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.
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Data availability
The data are available from the organizers of the Extreme Value Analysis 2021 Data Challenge, https://www.maths.ed.ac.uk/school-of-mathematics/eva-2021/competitions/data-challenge. Data are also available from the authors upon reasonable request and with permission of the organisers of the Data Challenge.
Code availability
Our model, training and evaluation code is available at https://github.com/Blackbox-EVA2021/CMIWAE.
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Acknowledgements
We thank Stjepan Šebek and Josip Žubrinić for valuable discussions and help in data preparation.
Funding
This research was partially supported by: Croatian Science Foundation (HRZZ) grant PZS-2019-02-3055 from “Research Cooperability” program funded by the European Social Fund.
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Ivek, T., Vlah, D. Reconstruction of incomplete wildfire data using deep generative models. Extremes 26, 251–271 (2023). https://doi.org/10.1007/s10687-022-00459-1
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DOI: https://doi.org/10.1007/s10687-022-00459-1