Abstract
In this paper we propose a procedure to estimate the distribution of wildfire frequency and severity using the wildfire data measured by month during 1993–2015. To this end, a spatial quantile autoregressive model (SQAR) is applied to the data with an aid of extreme value theory. Using the proposed method we are able to predict the distributional behavior of the data and identify the hidden structures beyond their mean structures. In addition, abundant interpretations are available with a regression-based model. We provide the estimated results from the wildfire data, including significant explanatory variables and some meaningful interpretations.
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Code availability
Codes for generating the results in the paper are available in https://github.com/JongminLee-stat/SQAR.
Data Availability
The datasets generated during and/or analysed during the current study are available from Optiz (2022) or from the corresponding author on reasonable request.
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Acknowledgements
The authors would like to thank Thomas Opitz for organizing the data competition for the 12th International conference on Extreme Value Analysis 2021. We also thank Hee-Seok Oh for encouraging us to participate in the data competition and for discussion. We moreover thank anonymous two referees and an Associate Editor for insightful comments.
Funding
Jongmin Lee is a beneficiary of an individual grant from CAINS supported by a KIAS Individual Grant (AP090201) via the Center for AI and Natural Sciences at Korea Institute for Advanced Study (KIAS). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1A4A1018207, NRF-2022R1C1C2003747).
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Jongmin Lee, Joonpyo Kim, Joonho Shin, Seongjin Cho, Seongmin Kim and Kyoungjae Lee declare that they have no conflicts of interest.
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Lee, J., Kim, J., Shin, J. et al. Analysis of wildfires and their extremes via spatial quantile autoregressive model. Extremes 26, 353–379 (2023). https://doi.org/10.1007/s10687-023-00462-0
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DOI: https://doi.org/10.1007/s10687-023-00462-0