AI Planning Enhances Intelligent Flood Response

This research proposes an intelligent framework designed to optimize flood response operations by employing temporal planning techniques. The system addresses the complex challenge of coordinating multiple actions across simultaneously flooded regions, considering severe resource limitations, the correct temporal sequencing of tasks, and efficient resource allocation. The framework integrates several critical components, including priority-driven triage for affected areas, dynamic assessment of route accessibility and travel costs, precise resource allocation, and comprehensive supply chain management. A key feature is its ability to perform mid-execution re-planning, allowing the system to adapt rapidly to unexpected environmental changes or new information during a flood event. Formulated in both Action Notation Modeling Language (ANML) and Planning Domain Definition Language (PDDL) 2.1, the framework ensures broad compatibility with various temporal planners. Experimental results confirm the feasibility and scalability of this approach, demonstrating that complex flood response scenarios can be effectively modeled and solved, while also offering guidance on selecting the most suitable planning tools.