Predictive Probability Density Mapping for Search and Rescue Using An Agent-Based Approach with Sparse Data

TL;DR

This paper presents an innovative agent-based model that predicts the probable locations of lost persons in search and rescue operations by simulating diverse psychological profiles and using Monte Carlo methods, validated with real-world data.

Contribution

The study introduces a novel agent-based approach that models lost persons' behavior without location-specific training, enhancing prediction accuracy in diverse environments.

Findings

Effective localization of lost persons in real-world scenarios

Outperforms traditional search prediction methods

Flexible model adaptable to various geographic regions

Abstract

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.