Bayesian methods for WiSAR
The SARBayes project uses the International Search & Rescue Incident Database (ISRID) [1] to study and forecast lost person behavior. To augment the predictive power of the project's models, we can supplement sparsely populated fields in ISRID with other sources of data. For instance, given an incident's date and location, we can pull data from online application programming interfaces (APIs) to fill in missing values for weather conditions such as temperature and precipitation.
Incident times follow a von Mises distribution centered near 5:30pm.
One goal of the SARBayes project is to forecast the probability of survival for lost persons. Such models could be useful in deciding to continue searching, and researchers making motion models can use survival predictions when generating probability maps of the lost person's location. We are analyzing data from the International Search & Rescue Incident Database (ISRID) to describe the probability of survival as a function of various features, such as age or temperature.