A novel method to identify likely causes of wildfire
Natural phenomena, such as wildfires, usually require the coincidence of several related factors in both time and space. In wildfire studies, literature-based factors were collected and listed in Mhawej et al. (2015). The question remains: which combination of factors leads to wildfires? In this con...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2017-01-01
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| Series: | Climate Risk Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2212096317300141 |
| _version_ | 1811209646724087808 |
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| author | Mario Mhawej Ghaleb Faour Jocelyne Adjizian-Gerard |
| author_facet | Mario Mhawej Ghaleb Faour Jocelyne Adjizian-Gerard |
| author_sort | Mario Mhawej |
| collection | DOAJ |
| description | Natural phenomena, such as wildfires, usually require the coincidence of several related factors in both time and space. In wildfire studies, literature-based factors were collected and listed in Mhawej et al. (2015). The question remains: which combination of factors leads to wildfires? In this context, a novel combination of wildfire likelihood factors was proposed in three different Lebanese forest covers (i.e., pine, oak, and mixed) and related literature-based factors to historical wildfire occurrences. The threshold values of each factor were deduced from the relationship between the element and number of fire occurrences. Each combination of factors was given a unique number. These mixtures corresponded to two, three, four or five factor groupings. The result was the association of each likelihood probability (i.e., low, medium, high, and very high) with different combinations of factors. Ultimately, using these combinations, the wildfire likelihood in Lebanese forests was efficiently and instantaneously generated. This approach could be portable to other Mediterranean regions and applied to several natural hazards. |
| first_indexed | 2024-04-12T04:42:43Z |
| format | Article |
| id | doaj.art-efec05ceaa3c4444a9c3857f72d58647 |
| institution | Directory Open Access Journal |
| issn | 2212-0963 |
| language | English |
| last_indexed | 2024-04-12T04:42:43Z |
| publishDate | 2017-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Climate Risk Management |
| spelling | doaj.art-efec05ceaa3c4444a9c3857f72d586472022-12-22T03:47:34ZengElsevierClimate Risk Management2212-09632017-01-0116C12013210.1016/j.crm.2017.01.004A novel method to identify likely causes of wildfireMario Mhawej0Ghaleb Faour1Jocelyne Adjizian-Gerard2National Center for Remote Sensing, National Council for Scientific Research (CNRS), Riad al Soloh, 1107 2260 Beirut, LebanonNational Center for Remote Sensing, National Council for Scientific Research (CNRS), Riad al Soloh, 1107 2260 Beirut, LebanonSt Joseph University, Department of Geography, Damascus Street, Mar Mickael, 1104 2020 Beirut, LebanonNatural phenomena, such as wildfires, usually require the coincidence of several related factors in both time and space. In wildfire studies, literature-based factors were collected and listed in Mhawej et al. (2015). The question remains: which combination of factors leads to wildfires? In this context, a novel combination of wildfire likelihood factors was proposed in three different Lebanese forest covers (i.e., pine, oak, and mixed) and related literature-based factors to historical wildfire occurrences. The threshold values of each factor were deduced from the relationship between the element and number of fire occurrences. Each combination of factors was given a unique number. These mixtures corresponded to two, three, four or five factor groupings. The result was the association of each likelihood probability (i.e., low, medium, high, and very high) with different combinations of factors. Ultimately, using these combinations, the wildfire likelihood in Lebanese forests was efficiently and instantaneously generated. This approach could be portable to other Mediterranean regions and applied to several natural hazards.http://www.sciencedirect.com/science/article/pii/S2212096317300141WildfireLikelihoodCombination of factorsPythonLebanonNatural hazard |
| spellingShingle | Mario Mhawej Ghaleb Faour Jocelyne Adjizian-Gerard A novel method to identify likely causes of wildfire Climate Risk Management Wildfire Likelihood Combination of factors Python Lebanon Natural hazard |
| title | A novel method to identify likely causes of wildfire |
| title_full | A novel method to identify likely causes of wildfire |
| title_fullStr | A novel method to identify likely causes of wildfire |
| title_full_unstemmed | A novel method to identify likely causes of wildfire |
| title_short | A novel method to identify likely causes of wildfire |
| title_sort | novel method to identify likely causes of wildfire |
| topic | Wildfire Likelihood Combination of factors Python Lebanon Natural hazard |
| url | http://www.sciencedirect.com/science/article/pii/S2212096317300141 |
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