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

Source: arXiv - 2602.08949v1

Overview

Wildfires are becoming more frequent and intense, and existing disaster‑management tools struggle to keep up because they rely on static simulations and passive data feeds. The authors propose the Intelligent Virtual Situation Room (IVSR) – a bidirectional digital‑twin platform powered by autonomous AI agents that creates a live, manipulable replica of a fire event and closes the loop between virtual analysis and real‑world actions.

Key Contributions

• Bidirectional Digital Twin for Wildfires – continuous ingestion of satellite, UAV, weather, and 3‑D forest data to maintain a real‑time virtual replica of the fire environment.
• Agentic AI Decision Layer – autonomous agents match live conditions to a pre‑computed Disaster Simulation Library, retrieve the most relevant response tactics, and suggest calibrated actions.
• Standardized Action‑Feedback Loop – expert‑approved recommendations (e.g., UAV redeployment, crew reallocation) are automatically translated into field‑ready procedures and fed back to the physical layer.
• Privacy‑Preserving Playback & Collider‑Based Fire‑Spread Projection – enables secure review of incidents and high‑fidelity physics‑based fire propagation modeling.
• Site‑Specific Machine‑Learning Retraining – the system fine‑tunes its predictive models on‑the‑fly using locally collected data, improving accuracy for each incident.
• Empirical Validation – case‑study simulations with an industrial partner show measurable reductions in detection‑to‑intervention latency and better resource coordination compared with legacy systems.

Methodology

1. Data Ingestion & Twin Construction – Sensors (satellite, aerial drones, ground stations) stream imagery, temperature, humidity, and wind vectors into a cloud‑native data lake. A 3‑D forest model (derived from LiDAR and GIS layers) is continuously updated to reflect fuel loads and terrain.
2. Similarity Engine – An AI‑driven similarity matcher compares the evolving twin state against thousands of pre‑simulated wildfire scenarios stored in a Disaster Simulation Library. The closest matches surface candidate response playbooks.
3. Agentic Decision Making – Autonomous agents evaluate the retrieved playbooks, adjust parameters (e.g., fire‑break placement, UAV flight paths) using reinforcement‑learning policies, and generate a ranked list of interventions.
4. Human‑in‑the‑Loop Validation – Domain experts review the AI‑suggested actions through an interactive dashboard, approve or tweak them, and trigger standardized SOPs.
5. Action Execution & Feedback – Approved actions are dispatched to field assets (UAVs, fire crews) via existing command‑and‑control APIs. Sensor feedback from these actions updates the twin, closing the loop for continuous adaptation.

Results & Findings

• Latency Reduction: Average time from fire detection to actionable recommendation dropped from ~12 minutes (baseline) to ~4 minutes in simulated deployments.
• Resource Efficiency: Optimized UAV routing saved ~22 % of flight time while maintaining coverage, allowing more assets to be allocated to high‑risk zones.
• Prediction Accuracy: Site‑specific ML retraining improved fire‑spread forecast error by 15 % over generic models.
• Scalability: The platform handled simultaneous modeling of up to 10 distinct fire fronts across a 500 km² region without degradation, demonstrating suitability for large‑scale incidents.

Practical Implications

• For Fire‑Management Agencies: IVSR can be integrated with existing incident‑command systems to provide near‑real‑time decision support, reducing reliance on manual map updates and post‑hoc analysis.
• For UAV Operators & Robotics Teams: The platform’s API exposes dynamic flight‑path recommendations, enabling autonomous drone fleets to react instantly to shifting fire fronts.
• For Cloud & Edge Infrastructure Providers: IVSR showcases a concrete use‑case for high‑throughput streaming ingestion, edge inference (e.g., on‑site fire detection), and distributed simulation workloads—opening opportunities for managed services tailored to disaster response.
• For AI/ML Practitioners: The agentic architecture (similarity engine + reinforcement‑learning policy) offers a reusable pattern for other time‑critical, safety‑critical domains such as flood or landslide management.

Limitations & Future Work

• Simulation Library Dependency: The quality of recommendations hinges on the breadth and fidelity of pre‑computed scenarios; expanding the library to cover rare weather‑fuel combinations remains an open challenge.
• Data Availability in Remote Areas: Real‑time sensor coverage can be sparse in rugged terrain, potentially degrading twin accuracy; future work will explore satellite‑only fallback modes and opportunistic crowd‑sourced data.
• Human Trust & Explainability: While experts can review AI suggestions, the system currently offers limited causal explanations for why a particular tactic was chosen; integrating interpretable AI modules is planned.
• Field Trials: Validation so far is simulation‑based; the authors aim to conduct live field exercises with fire‑fighting agencies to assess robustness under real operational constraints.

Authors

• Mohammad Morsali
• Siavash H. Khajavi

Paper Information

• arXiv ID: 2602.08949v1
• Categories: cs.AI, cs.SE
• Published: February 9, 2026
• PDF: Download PDF