Predicting Survivability in Lost Person Cases

Abstract

Over 600,000 people go missing each year in the United States. These events can cover situations anywhere from a young child going missing in a park to a group of hikers getting lost on a trail. dbS Productions has collected data on 16,863 searches over the past 30 years to generate an international database for use by search and rescue teams. The data recorded include a variety of fields such as subject category, terrain, sex, weight, and search hours. The data set is currently being underutilized by search and rescue teams due to a lack of applicable predictive tools built upon the aforementioned data. These search and rescue teams are also often volunteer-based and face great resource limitations in their operations. A tool is needed to predict the probability of a missing person’s survival for the operation’s coordinator to aid in resource allocation and the decision to continue or terminate search missions, which can be costly. This paper details an effort to create such a survivability predictor to help with this goal.We applied an Boosted Tree implementation of an Accelerated Failure Time (AFT) model to estimate the probability that a lost person would be found over time, given personal information about the subject, the location, and weather. We engineered several categorical variables and obtained weather data through the National Weather Service API to improve the model performance.Our engineered model recorded a C-index score of .67, which indicates a relatively robust model where industry standard considers 0.7 as "good" and 0.5 on par with random guessing. An analysis of the feature weights suggested that subject age, temperature, population density, mental fitness, and sex are the most critical indicators of survival in a missing person incident.Future work should involve incorporating more specific weather data, such as wind speeds and precipitation, into the model to improve prediction accuracy. Further research directions may include building a geo-spatial model to predict potential paths taken by a missing person based on initial location and the same predictors used in the survivability model.