[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126292-en":3,"doc-seo-126292-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126292,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","FIREfly Project - Forest Fire Monitoring and Prediction Using Machine Learning","Forest fires and extreme wildfire events threaten ecosystems worldwide, with Northern Thailand—especially Chiang Mai—facing severe impacts and pollution. This paper builds and evaluates machine learning models using publicly available satellite fire data and weather information. Random Forest and Support Vector Machines forecast forest fire occurrences with about 60% accuracy. Real-time predictions are presented via an integrated dashboard connected to Project FIREfly, enabling emergency services and the public to assess fire probability and take proactive mitigation measures.","FIREfly Project: Forest Fire Monitoring and Prediction Using  \nMachine Learning  \nSi Hui Onga, Hui Lun Anga, Elroy Liana, Timothy Tana, Kai Yang Tana, Emanuel Taya, Peter CY Yaua, Henrik Hesse*b, Kampol Woraditc and Dinesh Bhatiad  \na School of Computing Science, University of Glasgow, UK  \nb James Watt School of Engineering, University of Glasgow, UK c Department of Computer Engineering, Chiang Mai University, Thailand d School of Science, Engineering & Technology, RMIT University, Vietnam  \n* [Corresponding author: henrik.hesse@glasgow.ac.uk](Corresponding author: henrik.hesse@glasgow.ac.uk)  \nABSTRACT  \nForest fires and extreme wildfire events pose a major threat to ecosystems worldwide. This paper implements various machine learning algorithms for the prediction of forest fires in Northern Thailand, a region which is severely impacted by fire events and the resulting pollution. Using publicly available satellite data of fires and weather information, two prediction models, namely the Random Forest and Support Vector Machines, were developed and tested for their accuracy in forecasting forest fire occurrences. Initial results indicate that both models have an accuracy of approximately 60% in predicting forest fires. The real-time prediction data based on current weather conditions is further displayed in a dashboard. The online dashboard has been integrated with Project FIREfly which is a collaboration with Chiang Mai University and the University of Glasgow to visualize real-time data of forest fires. Through the integration of the predictive models, the online dashboard is able to show the probability of forest fires which improves situational awareness for emergency response services and enables them to take proactive measures in managing forest fires.  \nKeywords: Forest Fire Prediction, Machine Learning, Random Forest, Support Vector Machine, ML Dashboard  \n1. INTRODUCTION  \nForest fires are a major threat, causing widespread environmental damage, endangering lives and incurring significant economic losses. The Chiang Mai region is particularly vulnerable to Extreme Wildfire Events (EWE), highlighting the critical need for proactive measures to minimise the impact [1-2] . As part of Project FIREfly [3], this paper proposes the training of machine learning (ML) algorithms to predict EWE events in Northern Thailand and provide an early warning through a comprehensive dashboard application that is accessible to the general public. This work extends the ongoing effort of the FIREfly Project to develop a real-time monitoring system using drones to detect forest fires at the early onsets [4]. By integrating historical fire data and real-time meteorological information in this paper, the prediction model as well as the dashboard aims to provide a holistic view of potential fire risks combining prediction tools with real-life monitoring.  \nForest fires tend to manifest in a non-random manner, typically initiating and spreading in specific locations and under particular conditions. The onset and progression of a fire result from the joint influence of ignition agents – whether natural or human-induced – vegetation, weather conditions and human actions [5] . Weather conditions have a well-established and direct correlation with the occurrence and behaviour of forest fires. Thus, the focus on weather data allows for a targeted investigation into the immediate and significant contributors to forest fires. In addition, human actions are inherently complex and influenced by a multitude offactors, including societal norms and personal motivations. To initiate the ML-based tool for forest fire prediction, this paper will therefore employ only meteorological data to streamline the analysis and provide a starting point for this work. Using an AI-assisted segmentation tool developed in a separate work [6], we will extend the prediction tool using satellite and imagery data to consider other relevant factors, such as vegetation.  \nWhile pr","cbCairp32hecB9Bz","https://ap.wps.com/l/cbCairp32hecB9Bz","pdf",1704498,5,1,11,"English","en",105,"# Introduction\n## Methodology","[{\"question\":\"What data sources are used for forest fire prediction in this study?\",\"answer\":\"The models use publicly available satellite data of fires and corresponding weather information. Meteorological inputs include temperature, pressure, wind, gust, and humidity.\"},{\"question\":\"Which machine learning algorithms are developed and tested?\",\"answer\":\"The paper develops and tests Random Forest and Support Vector Machines (SVM). It compares their performance for forecasting forest fire occurrences.\"},{\"question\":\"How are prediction results delivered to users?\",\"answer\":\"The study displays real-time prediction outputs through an online dashboard. The dashboard is integrated with Project FIREfly to visualize live forest fire probabilities.\"}]","FIREfly Project - Forest Fire Monitoring and Prediction Using Machine Learning | PDF",1785904291,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"firefly-project-forest-fire-monitoring-and-prediction-using-machine-learning","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/firefly-project-forest-fire-monitoring-and-prediction-using-machine-learning/126292/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What data sources are used for forest fire prediction in this study?","Question",{"text":77,"@type":78},"The models use publicly available satellite data of fires and corresponding weather information. Meteorological inputs include temperature, pressure, wind, gust, and humidity.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which machine learning algorithms are developed and tested?",{"text":82,"@type":78},"The paper develops and tests Random Forest and Support Vector Machines (SVM). It compares their performance for forecasting forest fire occurrences.",{"name":84,"@type":75,"acceptedAnswer":85},"How are prediction results delivered to users?",{"text":86,"@type":78},"The study displays real-time prediction outputs through an online dashboard. 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