Artificial Intelligence, Autonomy, and Decision-Centric Systems for Wildfire Management
A Special Issue of Fire (ISSN 2571-6255).
Deadline for manuscript submissions: 28 February 2027 | Viewed by 237
Editors
Interests: wildfire suppression; fire support; decision support systems; command and con-trol; military tech-nology; crisis man-agement; operational planning; defense systems
Interests: operational meteor-ology; decision sup-port systems; envi-ronmental assess-ment; military tech-nology; resource planning; wildfire response; defense systems
Special Issue Information
Dear Colleagues,
Wildfires are becoming more frequent, intense, and operationally complex due to climate change, land-use change, and increasing human activity in fire-prone environments. At the same time, wildfire agencies face growing pressure to make faster, better-informed decisions under conditions of uncertainty, limited resources, and rapidly evolving fire behavior. While substantial progress has been made in wildfire detection, monitoring, and sensing technologies, a critical challenge remains the translation of heterogeneous data and advanced analytics into timely, reliable, and actionable operational decisions.
This Special Issue therefore focuses specifically on novel and innovative artificial intelligence (AI) approaches, autonomous systems, and decision-centric digital technologies that strengthen command, coordination, and decision support across the wildfire management cycle. Rather than primarily emphasizing conventional fire detection, UAV-centered monitoring, or routine remote-sensing applications, the issue seeks contributions that advance higher-level operational intelligence for wildfire management. Relevant topics include AI-enabled decision support; uncertainty-aware forecasting and scenario analysis; resource prioritization and allocation; human–AI teaming; digital command-and-control architectures; autonomous and semi-autonomous coordination systems; simulation and digital-twin environments for planning, training, and operational evaluation; and interoperable data architectures that improve situational awareness and operational resilience.
The Special Issue especially welcomes interdisciplinary studies that adapt methods from robotics, defense systems, operations research, and advanced autonomous decision environments to wildfire management. Contributions should not only demonstrate technical innovation but also clarify how emerging technologies improve operational effectiveness, coordination, resilience, and decision quality in real or realistic wildfire contexts.
Dr. Martin Blaha
Dr. Michal Šustr
Dr. Jan Ivan
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fire is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- wildfire management
- decision support
- artificial intelligence
- autonomous systems
- human–AI teaming
- command and control
- situational awareness
- resource allocation
- uncertainty-aware forecasting
- operational resilience
- digital twin
- simulation and modeling
- interoperability
- multimodal decision support
- autonomous coordination
- operational intelligence
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