Using an agent-based model to identify high probability search areas for search and rescue

Abstract

Thousands of people become lost in the wilderness each year and search and rescue personnel are called in to search for and to locate people who are lost. Time is critical as the lost person's chance of survival decreases over time. One method of improving search outcomes is efficient and accurate planning of search areas. Search and rescue planning techniques have been developed over time through extensive training, experience and knowledge. To expedite the search area planning process, an agent-based model (ABM) was used to highlight probabilistic and evidence-based areas typically considered by search area planners. This model takes spatial data calculated to a time-cost raster and incorporates lost person characteristics to determine location-specific probability data that can be used in decision-making.