A Context-Aware Post-Earthquake Search and Rescue with ACO-RL

Abstract

Natural hazards such as earthquakes can cause significant loss of life and extensive damage worldwide each year. Search and Rescue (SAR) operations in such critical situations require an urgent need for speed and efficiency, but are also complex challenges, given their dynamic and uncertain nature. This challenge requires approaches that integrate realistic environmental modeling with intelligent decision-making. Geospatial information systems are a powerful tool that can provide robust representations of complex terrains under time-critical circumstances, and artificial intelligence techniques can simplify decision-making and optimize decision-making under time-sensitive circumstances. In addition, context-aware SAR enables the precise identification and quantification of situational parameters, allowing better tailoring of operations to the real world. A context-aware SAR framework was developed in this study to identify and estimate key parameters in rescue operations, activities, and environmental conditions, and to model the problem within a spatial information system. And by integrating ant colony optimization (ACO), which is well-suited to modeling cooperative search behaviors, with the SARSA reinforcement learning algorithm, which is well-suited to discrete decision-making environments, a hybrid method was proposed. As part of this integration, a pheromone-like reinforcement mechanism was utilized to enhance overall performance and adaptability. A comparison of this hybrid approach against a standalone ACO model revealed that it outperformed it in both operational efficiency and solution quality, underscoring its potential for real-world SAR applications.