[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124427-en":3,"doc-seo-124427-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124427,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Global Lightning-Ignited Wildfires Prediction and Climate Change Projections based on Explainable Machine Learning Models","Wildfires are a major natural-disaster hazard and an accelerating contributor to climate change, while climate change in turn alters wildfire behavior. Lightning-ignited fires, though less common than human-caused ignitions globally, can dominate burned area in some regions and drive substantial carbon emissions. Existing machine-learning predictors often target specific areas, limiting global transferability. This work builds global explainable ML models to classify lightning- versus anthropogenic wildfires and to estimate ignition probability from meteorology and vegetation. Model-driven analyses reveal strong global differences and increasing lightning-ignited wildfire risk under climate change, supporting the need for type-specific predictive models and fire-weather indices.","arXiv :2409 . 10046v1 [ cs .LG] 16 Sep 2024  \nGlobal Lightning-Ignited Wildfires Prediction and Climate Change Projections based on Explainable Machine Learning Models  \nAssaf Shmuel 1,* , Teddy Lazebnik2,3 , Oren Glickman 1 , Eyal Heifetz4 , and Colin Price4  \n1 Department of Computer Science, Bar Ilan University, Ramat Gan, Israel  \n2 Department of Mathematics, Ariel University, Ariel, Israel  \n3 Department of Cancer Biology, Cancer Institute, University College London, London, UK  \n4 Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv, Israel  \n* Corresponding author: [assafshmuel91@gmail.com](assafshmuel91@gmail.com)  \nABSTRACT  \nWildfires pose a significant natural disaster risk to populations and contribute to accelerated climate change. As wildfires are also affected by climate change, extreme wildfires are becoming increasingly frequent. Although they occur less frequently globally than those sparked by human activities, lightning-ignited wildfires play a substantial role in carbon emissions and account for the majority of burned areas in certain regions. While existing computational models, especially those based on machine learning, aim to predict lightning-ignited wildfires, they are typically tailored to specific regions with unique characteristics, limiting their global applicability. In this study, we present machine learning models designed to characterize and predict lightning-ignited wildfires on a global scale. Our approach involves classifying lightning-ignited versus anthropogenic wildfires, and estimating with high accuracy the probability of lightning to ignite a fire based on a wide spectrum of factors such as meteorological conditions and vegetation. Utilizing these models, we analyze seasonal and spatial trends in lightning-ignited wildfires shedding light on the impact of climate change on this phenomenon. We analyze the influence of various features on the models using eXplainable Artificial Intelligence (XAI) frameworks. Our findings highlight significant global differences between anthropogenic and lightning-ignited wildfires. Moreover, we demonstrate that, even over a short time span of less thana decade, climate changes have steadily increased the global risk of lightning-ignited wildfires. This distinction underscores the imperative need for dedicated predictive models and fire weather indices tailored specifically to each type of wildfire.  \nIntroduction  \nWildfires are among the most hazardous natural disasters on Earth. Although studies have found a decrease in global burned areas due to anthropogenic activity 1 , the frequency of extratropical lightning-ignited wildfires appears to be on the rise asthe Earth’s climate changes2. For example, the frequency of extreme lightning-ignited wildfires has drastically increased in California in the last decades3. Lightning is the main cause for wildfire activity in high latitudes both in terms of wildfire occurrence and in terms of burned areas4. This trend is of considerable concern, as extratropical forests are responsible for a substantial portion of carbon emissions. Lightning-ignited wildfires differ from anthropogenic wildfires in several aspects; for example, lightning-ignited wildfires tend to ignite in remote locations in which firefighters have difficulty extinguishing them before they evolve into extreme dimensions. These fires also tend to ignite in clusters5 while forest managers are less likely to fight these fires if they are not impacting settlements and infrastructures. While recent studies have developed machine learning (ML) models to predict wildfires on a global scale6 , the different nature of lightning-ignited wildfires requires dedicated models to predict and analyze them separately.  \nWildfires can be ignited by cloud-to-ground (CG) lightning strikes. As lighting ignitions occur in thunderstorms, they are often accompanied by precipitation7. However, lightning strokes that occur with little preci","cbCaitO0eLq13rwu","https://ap.wps.com/l/cbCaitO0eLq13rwu","pdf",8080297,1,15,"English","en",105,"# Abstract\n# Introduction\n## Lightning and dry lightning mechanisms\n## Holdover wildfire phenomenon\n## Related global modeling efforts\n## Motivation for dedicated lightning-ignited models","[{\"question\":\"Why do lightning-ignited wildfires matter for both disasters and climate change?\",\"answer\":\"Lightning-ignited wildfires increase natural-disaster risk and contribute to accelerated climate change through carbon emissions. Even when less frequent than human-caused fires globally, they can dominate burned area in some regions.\"},{\"question\":\"How does the study predict lightning ignition likelihood?\",\"answer\":\"It uses machine learning models that classify lightning-ignited versus anthropogenic wildfires and estimate the probability that lightning ignites a fire from multiple factors, including meteorological conditions and vegetation.\"},{\"question\":\"What do the explainable AI analyses show about wildfire behavior under climate change?\",\"answer\":\"The findings highlight significant global differences between lightning-ignited and anthropogenic wildfires and show that, over a short period of under a decade, climate changes steadily raise the global risk of lightning-ignited wildfires.\"}]","Global Lightning-Ignited Wildfires Prediction and Climate Change Projections based on Explainable Machine Learning Models | PDF",1785822250,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"global-lightning-ignited-wildfires-prediction-and-climate-change-projections-based-on-explainable-machine-learning-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/global-lightning-ignited-wildfires-prediction-and-climate-change-projections-based-on-explainable-machine-learning-models/124427/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do lightning-ignited wildfires matter for both disasters and climate change?","Question",{"text":75,"@type":76},"Lightning-ignited wildfires increase natural-disaster risk and contribute to accelerated climate change through carbon emissions. Even when less frequent than human-caused fires globally, they can dominate burned area in some regions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study predict lightning ignition likelihood?",{"text":80,"@type":76},"It uses machine learning models that classify lightning-ignited versus anthropogenic wildfires and estimate the probability that lightning ignites a fire from multiple factors, including meteorological conditions and vegetation.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the explainable AI analyses show about wildfire behavior under climate change?",{"text":84,"@type":76},"The findings highlight significant global differences between lightning-ignited and anthropogenic wildfires and show that, over a short period of under a decade, climate changes steadily raise the global risk of lightning-ignited wildfires.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]