[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118855-en":3,"doc-seo-118855-105":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":4,"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},118855,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A Brief Review of Machine Learning Algorithms in Forest Fires Science - read online free","Forest fires increasingly threaten ecosystems and economies worldwide, making early prediction and detection essential. The article surveys artificial intelligence methods used to identify and forecast forest fires and to estimate the likelihood and potential damage. It explains that selecting an appropriate machine learning model is a trade-off because each algorithm offers specific strengths and limitations. The review focuses on research gaps and recent studies, emphasizing that choosing techniques aligned with forest characteristics improves predictive power and supports more effective fire management decisions.","applied sciences  \nReview  \nA Brief Review of Machine Learning Algorithms in Forest Fires Science  \nRamez Alkhatib 1, *, Wahib Sahwan 1, Anas Alkhatieb 2 and Brigitta Schütt 1  \nCitation: Alkhatib, R.; Sahwan, W.; Alkhatieb, A.; Schütt, B. A Brief Review of Machine Learning Algorithms in Forest Fires Science. Appl. Sci. 2023, 13, 8275. [https://](https://)[ ](https://)[doi.org/10.3390/app13148275](doi.org/10.3390/app13148275)  \nAcademic Editors: Andrea L. Rizzo, Na Zhao and Hua Zhong  \nReceived: 31 May 2023  \nRevised: 13 July 2023  \nAccepted: 14 July 2023  \nPublished: 17 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Physical Geography, Institute of Geographical Sciences, Free University Berlin, Malteserstraße 74-100, 12449 Berlin, Germany; [wahib.sahwan@fu-berlin.de](wahib.sahwan@fu-berlin.de) (W.S.); [brigitta.schuett@fu-berlin.de](brigitta.schuett@fu-berlin.de) (B.S.)  \n2 College of Computer & Information Systems, Umm al Qura University, Makkah 24382, Saudi Arabia; [alkhatieb78@gmail.com](alkhatieb78@gmail.com)  \n* Correspondence: [ramez.alkhatib@fu-berlin.de](ramez.alkhatib@fu-berlin.de)  \nAbstract: Due to the harm forest ﬁres cause to the environment and the economy as they occur more frequently around the world, early ﬁre prediction and detection are necessary. To anticipate and discover forest ﬁres, several technologies and techniques were put forth. To forecast the likelihood of forest ﬁres and evaluate the risk of forest ﬁre-induced damage, artiﬁcial intelligence techniques are a crucial enabling technology. In current times, there has been a lot of interest in machine learning techniques. The machine learning methods that are used to identify and forecast forest ﬁres are reviewed in this article. Selecting the best forecasting model is a constant gamble because each ML algorithm has advantages and disadvantages. Our main goal is to discover the research gaps and recent studies that use machine learning techniques to study forest ﬁres. By choosing the best ML techniques based on particular forest characteristics, the current research results boost prediction power.  \nKeywords: machine learning; forest ﬁres; wildﬁre; deep learning; drone; UAV; remote sensing; Google Earth Engine (GEE)  \n1. Introduction  \nForest ﬁres are a ubiquitous and vital component of the Earth's system [1], and it is a year-round worldwide phenomenon that happens each month (Figure 1) . According to [2], the current estimate of the annual worldwide area burned is around 420 Mha, which is more than the area of India. Grasslands and savannas account are the areas most affected by forest ﬁres. People start over 90% of forest ﬁres, and ﬂash of lightning is to blame for most of the leftover ignitions. Humans may suffer severe effects from forest ﬁres, directly through fatalities and community devastation or indirectly through smoke and ash inhalation [3] .  \nForest ﬁres have ramiﬁcations for global warming and the survival of ﬂora and fauna [3] . Early ﬁre prediction and identiﬁcation are crucial to limit damage and reduce ﬁreﬁghting efforts. Millions are spent annually on ﬁre management efforts to reduce or stop forest ﬁres [3] . Therefore, it is essential to comprehend forest ﬁres and their triggersand to improve forest ﬁre prediction in several vital areas of forest ﬁre management.  \nTwo main measures are to be taken to prevent forest ﬁres. The ﬁrst is forest ﬁre incidence prediction, which essentially forecasts the forest ﬁre eruption likelihood earlier in its early ignition by modeling the relationship between the ﬁre risk and signiﬁcant factors, for example, fuel content or weather conditions. The second measure is forest ﬁre","cbCaim739ZcWjrzf","https://ap.wps.com/l/cbCaim739ZcWjrzf","pdf",2021910,1,15,"English","en",105,"# Introduction\n## Forest fire incidence prediction\n## Forest fire detection\n# Overview of machine learning approaches\n## Model selection trade-offs\n# Research gaps and recent studies\n## Aligning ML techniques with forest characteristics","[{\"question\":\"Why is early forest fire prediction and detection necessary?\",\"answer\":\"Forest fires occur more frequently worldwide and cause serious harm to the environment and the economy. Early prediction and detection help limit damage and reduce firefighting efforts.\"},{\"question\":\"What is the main focus of the review article?\",\"answer\":\"The review surveys machine learning algorithms used to identify and forecast forest fires, and highlights research gaps and recent studies in this area.\"},{\"question\":\"Why is choosing the best forecasting model considered difficult?\",\"answer\":\"Because each machine learning algorithm has advantages and disadvantages, so selecting the best model depends on the specific context and requirements.\"}]","A Brief Review of Machine Learning Algorithms in Forest Fires Science - read online free | PDF",1785720638,38,{"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},"a-brief-review-of-machine-learning-algorithms-in-forest-fires-science-read-online-free","",{"@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/a-brief-review-of-machine-learning-algorithms-in-forest-fires-science-read-online-free/118855/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early forest fire prediction and detection necessary?","Question",{"text":75,"@type":76},"Forest fires occur more frequently worldwide and cause serious harm to the environment and the economy. 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