A new method of predicting where lost people may be found could help mountain rescue teams save lives
A computer model that “thinks” like a missing person could help mountain rescue teams save lives, researchers from the University of Glasgow have found.
The new method predicts where people lost in the wilderness may be found by imitating their decision-making process. The sophisticated computer system models the actions of simulated people lost in outdoor environments.
The system, based on data drawn from accounts of how people in the real world behaved after finding themselves lost outdoors, creates a ‘heat map’ showing where missing people may be found in any landscape.
It is hoped the findings could lead to a robust new method to help search and rescue teams choose where to focus their recovery efforts in future.
“I grew up in the rural Highlands, and I’m a keen hillwalker, so I’m very conscious of both how dangerous hiking can be and what incredible work search and rescue teams do,” Jan-Hendrik Ewers, lead researchers on the project, said.
“Search and rescue teams perform vitally important lifesaving work, despite being frequently under-funded and often being crewed by volunteers.
“Initially, as part of my PhD, I set out to see whether it would be possible to use machine learning to train a new type of search and rescue system to predict where lost hikers might be found.
“However, machine learning requires a vast amount of information to draw its conclusions.
“The limited resources of search teams mean they are rightly more focused on saving lives than capturing data on every aspect of their search missions, so there wasn’t enough information for us to make that approach work.
“That led my colleagues and I to consider whether we could tap into existing research on the behaviour of missing people which aims to understand their choices about where they went and why.”
The team used data from historic studies of how lost people behaved in real-world situations to create simulated ‘agents’ who act based on different psychological states.
The algorithms which underpin the agents are guided by distinct sub-models, each with a different goal in mind. They all seek to find their way back to civilisation by heading for either water, trees, buildings, paths or roads.
The simulated agents make decisions about where to go based on factors like their current location and whether they could see their preferred terrain.
The team’s system also looked at data relating to missing peoples’ likelihood of being found in different types of terrain, and the distances people typically travelled from their reported last known location.
Dr David Anderson, co-author of the paper, explained: “One advantage of this kind of psychological modelling approach to locating missing people is that it could potentially be applied to any landscape.
“That means it could help search and rescue teams around the world, no matter if they’re working in the mountains, jungles, or deserts.
“More work will be required before it could be used in real-world situations, but these are really encouraging results.”
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