Next-Gen Forest Fire Management: Integrating Deep Learning with GIS and Earth Observation for Early Warning Systems

Next-Gen Forest Fire Management: Integrating Deep Learning with GIS and Earth Observation for Early Warning Systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Next-Gen Forest Fire Management: Integrating Deep Learning with GIS and Earth Observation for Early Warning Systems Dilbar Hussain, Turkia Almoustafa, Rizwan Ahmed, Saba Shakoor, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6888318/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Change in Climate, environmental mismanagement and human invasion, caused wildfires to transform from unusual natural disturbances into devastating worldwide dangers. Innovative, data-driven solutions are required for early detection, prediction, and proactive mitigation of wildfires, as traditional methods that rely on manual observation and static risk indicators are becoming less and less effective. The split data, slow response times, and poor forecasting accuracy of current wildfire control systems make it unable to handle the growing size and unpredictability of contemporary wildfires. This study presents a next-generation forest fire management approach that combines Earth Observation (EO) and Geographic Information Systems (GIS) data with deep learning (XGBoost, Deep Neural Networks). The approach creates dynamic risk assessments by integrating multi-source datasets, such as terrain, fuel, climate, and real-time IoT sensor inputs, within an explainable AI (XAI) framework (SHAP, LIME). Drone-based remote sensing and high-resolution satellite imagery, enable continuous monitoring, while ensemble models improve prediction reliability through historical and real-time data calibration. The proposed system demonstrates enhanced accuracy in early fire detection, dynamic risk mapping, and predictive analytics, enabling timely interventions. The integration of AI with geospatial technologies also supports long-term resilience planning, including post-fire recovery and adaptive land-use strategies. By bridging advanced computational intelligence with geospatial and Earth Observation technologies, this research advances the transition from reactive firefighting to proactive, intelligent wildfire management. The framework offers a scalable, real-time early warning system, contributing to global efforts in mitigating wildfire risks amid escalating climate challenges. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Physical sciences/Engineering Physical sciences/Mathematics and computing Wildfire management Deep Learning Remote Sensing Explainable AI Early Warning Systems Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6888318","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":478036359,"identity":"839ad340-e578-4f63-9c31-337b897b5804","order_by":0,"name":"Dilbar Hussain","email":"","orcid":"","institution":"Iqra University Main Campus Karachi","correspondingAuthor":false,"prefix":"","firstName":"Dilbar","middleName":"","lastName":"Hussain","suffix":""},{"id":478036360,"identity":"feb9d2a4-07cd-4cec-96ee-d62c1de81b1e","order_by":1,"name":"Turkia 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Innovative, data-driven solutions are required for early detection, prediction, and proactive mitigation of wildfires, as traditional methods that rely on manual observation and static risk indicators are becoming less and less effective. The split data, slow response times, and poor forecasting accuracy of current wildfire control systems make it unable to handle the growing size and unpredictability of contemporary wildfires. This study presents a next-generation forest fire management approach that combines Earth Observation (EO) and Geographic Information Systems (GIS) data with deep learning (XGBoost, Deep Neural Networks). The approach creates dynamic risk assessments by integrating multi-source datasets, such as terrain, fuel, climate, and real-time IoT sensor inputs, within an explainable AI (XAI) framework (SHAP, LIME). 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