Fire weather indices tailored to regional patterns outperform global models
Abstract Fire weather indices (FWIs) are widely used to assess wildfire risk, but are typically designed for specific regions and not adapted globally. Here, we present a systematic effort to generate country-specific FWIs that capture regional fire-weather patterns. We evaluate three widely used in...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Natural Hazards |
| Online Access: | https://doi.org/10.1038/s44304-025-00126-y |
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| _version_ | 1849333793843838976 |
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| author | Assaf Shmuel Teddy Lazebnik Eyal Heifetz Oren Glickman Colin Price |
| author_facet | Assaf Shmuel Teddy Lazebnik Eyal Heifetz Oren Glickman Colin Price |
| author_sort | Assaf Shmuel |
| collection | DOAJ |
| description | Abstract Fire weather indices (FWIs) are widely used to assess wildfire risk, but are typically designed for specific regions and not adapted globally. Here, we present a systematic effort to generate country-specific FWIs that capture regional fire-weather patterns. We evaluate three widely used indices across countries, finding that the Canadian FWI performs best overall (ROC AUC of 0.69). Tailoring the index to each country with a Genetic Algorithm significantly improves its accuracy, raising the ROC AUC from 0.69 to 0.79. To further improve accuracy while maintaining interpretability, we develop a single Decision Tree model per country, achieving an ROC AUC of 0.86. Attempts to develop a single global Decision Tree yielded substantially lower accuracy, highlighting the limitations of universal models and the importance of capturing regional characteristics such as weather patterns, vegetation types, and topography for accurately predicting wildfire risk. Adapting FWIs regionally is crucial under accelerating climate change conditions. |
| format | Article |
| id | doaj-art-7835c3b254e14a9987fa3699884dccd2 |
| institution | Kabale University |
| issn | 2948-2100 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Natural Hazards |
| spelling | doaj-art-7835c3b254e14a9987fa3699884dccd22025-08-20T03:45:45ZengNature Portfolionpj Natural Hazards2948-21002025-07-01211910.1038/s44304-025-00126-yFire weather indices tailored to regional patterns outperform global modelsAssaf Shmuel0Teddy Lazebnik1Eyal Heifetz2Oren Glickman3Colin Price4Department of Geophysics, Tel Aviv UniversityDepartment of Information Systems, University of HaifaDepartment of Geophysics, Tel Aviv UniversityDepartment of Computer Science, Bar Ilan UniversityDepartment of Geophysics, Tel Aviv UniversityAbstract Fire weather indices (FWIs) are widely used to assess wildfire risk, but are typically designed for specific regions and not adapted globally. Here, we present a systematic effort to generate country-specific FWIs that capture regional fire-weather patterns. We evaluate three widely used indices across countries, finding that the Canadian FWI performs best overall (ROC AUC of 0.69). Tailoring the index to each country with a Genetic Algorithm significantly improves its accuracy, raising the ROC AUC from 0.69 to 0.79. To further improve accuracy while maintaining interpretability, we develop a single Decision Tree model per country, achieving an ROC AUC of 0.86. Attempts to develop a single global Decision Tree yielded substantially lower accuracy, highlighting the limitations of universal models and the importance of capturing regional characteristics such as weather patterns, vegetation types, and topography for accurately predicting wildfire risk. Adapting FWIs regionally is crucial under accelerating climate change conditions.https://doi.org/10.1038/s44304-025-00126-y |
| spellingShingle | Assaf Shmuel Teddy Lazebnik Eyal Heifetz Oren Glickman Colin Price Fire weather indices tailored to regional patterns outperform global models npj Natural Hazards |
| title | Fire weather indices tailored to regional patterns outperform global models |
| title_full | Fire weather indices tailored to regional patterns outperform global models |
| title_fullStr | Fire weather indices tailored to regional patterns outperform global models |
| title_full_unstemmed | Fire weather indices tailored to regional patterns outperform global models |
| title_short | Fire weather indices tailored to regional patterns outperform global models |
| title_sort | fire weather indices tailored to regional patterns outperform global models |
| url | https://doi.org/10.1038/s44304-025-00126-y |
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