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Digital Twin and Agentic AI for Wild Fire Disaster Management: Intelligent Virtual Situation Room

Mohammad Morsali, Siavash H. Khajavi

TL;DR

This paper introduces the Intelligent Virtual Situation Room (IVSR), a bidirectional Digital Twin augmented by autonomous AI agents to manage wildfires through real-time multisource data fusion and a precomputed Disaster Simulation Library. By matching live conditions to archived intervention scenarios and routing executable plans back to the field, IVSR provides proactive, semi-automated decision support with expert oversight, reducing detection-to-intervention latency and improving resource coordination. The work integrates a suite of methods, including 3D sensor coverage optimization, 3D fire localization, privacy-preserving incident rollback, collider-based fire-spread simulation, and a conceptual IVSR architecture with a DSL and similarity-based retrieval. It also discusses Human-In-The-Loop governance, data interoperability challenges, and the pathway toward scalable field trials, positioning IVSR as a scalable, resilient framework for real-time wildfire disaster management.

Abstract

According to the United Nations, wildfire frequency and intensity are projected to increase by approximately 14% by 2030 and 30% by 2050 due to global warming, posing critical threats to life, infrastructure, and ecosystems. Conventional disaster management frameworks rely on static simulations and passive data acquisition, hindering their ability to adapt to arbitrarily evolving wildfire episodes in real-time. To address these limitations, we introduce the Intelligent Virtual Situation Room (IVSR), a bidirectional Digital Twin (DT) platform augmented by autonomous AI agents. The IVSR continuously ingests multisource sensor imagery, weather data, and 3D forest models to create a live virtual replica of the fire environment. A similarity engine powered by AI aligns emerging conditions with a precomputed Disaster Simulation Library, retrieving and calibrating intervention tactics under the watchful eyes of experts. Authorized action-ranging from UAV redeployment to crew reallocation-is cycled back through standardized procedures to the physical layer, completing the loop between response and analysis. We validate IVSR through detailed case-study simulations provided by an industrial partner, demonstrating capabilities in localized incident detection, privacy-preserving playback, collider-based fire-spread projection, and site-specific ML retraining. Our results indicate marked reductions in detection-to-intervention latency and more effective resource coordination versus traditional systems. By uniting real-time bidirectional DTs with agentic AI, IVSR offers a scalable, semi-automated decision-support paradigm for proactive, adaptive wildfire disaster management.

Digital Twin and Agentic AI for Wild Fire Disaster Management: Intelligent Virtual Situation Room

TL;DR

This paper introduces the Intelligent Virtual Situation Room (IVSR), a bidirectional Digital Twin augmented by autonomous AI agents to manage wildfires through real-time multisource data fusion and a precomputed Disaster Simulation Library. By matching live conditions to archived intervention scenarios and routing executable plans back to the field, IVSR provides proactive, semi-automated decision support with expert oversight, reducing detection-to-intervention latency and improving resource coordination. The work integrates a suite of methods, including 3D sensor coverage optimization, 3D fire localization, privacy-preserving incident rollback, collider-based fire-spread simulation, and a conceptual IVSR architecture with a DSL and similarity-based retrieval. It also discusses Human-In-The-Loop governance, data interoperability challenges, and the pathway toward scalable field trials, positioning IVSR as a scalable, resilient framework for real-time wildfire disaster management.

Abstract

According to the United Nations, wildfire frequency and intensity are projected to increase by approximately 14% by 2030 and 30% by 2050 due to global warming, posing critical threats to life, infrastructure, and ecosystems. Conventional disaster management frameworks rely on static simulations and passive data acquisition, hindering their ability to adapt to arbitrarily evolving wildfire episodes in real-time. To address these limitations, we introduce the Intelligent Virtual Situation Room (IVSR), a bidirectional Digital Twin (DT) platform augmented by autonomous AI agents. The IVSR continuously ingests multisource sensor imagery, weather data, and 3D forest models to create a live virtual replica of the fire environment. A similarity engine powered by AI aligns emerging conditions with a precomputed Disaster Simulation Library, retrieving and calibrating intervention tactics under the watchful eyes of experts. Authorized action-ranging from UAV redeployment to crew reallocation-is cycled back through standardized procedures to the physical layer, completing the loop between response and analysis. We validate IVSR through detailed case-study simulations provided by an industrial partner, demonstrating capabilities in localized incident detection, privacy-preserving playback, collider-based fire-spread projection, and site-specific ML retraining. Our results indicate marked reductions in detection-to-intervention latency and more effective resource coordination versus traditional systems. By uniting real-time bidirectional DTs with agentic AI, IVSR offers a scalable, semi-automated decision-support paradigm for proactive, adaptive wildfire disaster management.
Paper Structure (22 sections, 8 figures, 6 tables)

This paper contains 22 sections, 8 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Digital Cities
  4. Bidirectional Digital Twin: Concepts and Architectures
  5. Wild Fire Disaster Management Using DT
  6. AI Agent Architectures and Approaches
  7. Situation Room in Crisis Management
  8. Gap in the Literature
  9. Methods & Materials
  10. Sensors 3D Coverage Optimization
  11. Fire Localization in 3D Model
  12. Incident Rollback & Anonymization of Incident Footage
  13. Fire Spread Simulation & Customization of Machine Learning Models
  14. Physical Intervention & Issuing Commands
  15. The Conceptual Architecture of Intelligent Virtual Situation Room
  16. ...and 7 more sections

Figures (8)

  • Figure 1: Overview of the DT use case for disaster management
  • Figure 2: Research Method Structure
  • Figure 3: Visualization of intersections in the scene from 100 rays emitted by the virtual camera.
  • Figure 4: Calculated camera coverage area as a percentage.
  • Figure 5: Example of flame location points in the virtual camera and the generated flame position in the scene.
  • ...and 3 more figures