Predictive Probability Density Mapping for Search and Rescue Using An Agent-Based Approach with Sparse Data
This work addresses search and rescue operations with limited resources, offering a flexible and adaptable approach, though it appears incremental as it builds on existing agent-based and simulation techniques.
The paper tackles the problem of predicting lost person locations in search and rescue by developing an agent-based model that simulates diverse psychological profiles to navigate landscapes autonomously, with validation showing promising results compared to alternative methods.
Predicting the location where a lost person could be found is crucial for search and rescue operations with limited resources. To improve the precision and efficiency of these predictions, simulated agents can be created to emulate the behavior of the lost person. Within this study, we introduce an innovative agent-based model designed to replicate diverse psychological profiles of lost persons, allowing these agents to navigate real-world landscapes while making decisions autonomously without the need for location-specific training. The probability distribution map depicting the potential location of the lost person emerges through a combination of Monte Carlo simulations and mobility-time-based sampling. Validation of the model is achieved using real-world Search and Rescue data to train a Gaussian Process model. This allows generalization of the data to sample initial starting points for the agents during validation. Comparative analysis with historical data showcases promising outcomes relative to alternative methods. This work introduces a flexible agent that can be employed in search and rescue operations, offering adaptability across various geographical locations.